Engineering Innovations Volume 4

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Volume 4 of
Engineering Innovations
ISSN print 2813-0995
ISSN web 2813-1002

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Table of Contents

Finite Element Analysis of Damage of Cylindrical Roller Bearing of Main Shaft of Shot Blasting Machine

Yi Jia Zhang, Yong Zhi Pan, Zheng Feng Jiang, Xiu Li Fu, Tian Yi Zhang

Vibrational analysis of Mini - Unmanned Aerial Vehicles Due to Gun Recoil

N. Rino Nelson, P.M. Siva

Artificial Intelligence Predictions Effect of Loading Rate, Crack Width and Crack Length Ratio on Mode I Fracture Toughness of PMMA

Attasit Wiangkham, Prasert Aengchuan, Atthaphon Ariyarit

Optimization of a Developed Multi-Cyclone Using Response Surface Methodology (RSM) to Control Fine Particulate Emission

Nur Hasyimah, M. Rashid, H. Norelyza

Tensile Strength of 3D Printing Scaffold Design Truncated Hexahedron for Tuberculosis Drug Delivery

Eka Yuliatin, Dyah Hikmawati, Aminatun Aminatun, Aniek Setiya Budiatin, Prihartini Widiyanti, Frazna Parastuti

A Novel Epitope Dataset: Performance of the MCL-Based Algorithms to Generate Dataset for Graph Learning Model

Binti Solihah, Aina Musdholifah, Azhari Azhari

A Novel Ensemble of Support Vector Machines for Improving Medical Data Classification

Phuoc Hai Huynh, Van Hoa Nguyen

Keywords Index

Authors Index

The original version of this eBook is available on https://www.scientific.net/book/engineering-innovations-vol-4/978-3-0364-1008-1

Engineering Innovations, ISSN: 2813-1002, Vol. 4, pp 1-8

doi: 10.4028/p-391e5g

© 2023 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2022-06-20

Accepted:
2022-06-21

Online:
2023-02-15

Finite Element Analysis of Damage of Cylindrical Roller Bearing of Main Shaft of Shot Blasting Machine

The original version of this paper is available on https://www.scientific.net/EI.4.1.pdf

Zhang Yijia , Pan Yongzhi , Jiang Zhengfeng , Fu Xiuli , Zhang Tianyi

School of Mechanical Engineering, University of Jinan, China

Zibo TAA Metal Technology Co., Ltd., China

, me_panyz@ujn.edu.cn, , me_fuxl@ujn.edu.cn, 1364711071@qq.com

Keywords: Shot blasting machine; Cylindrical roller bearing; Thermal-structural coupling; Damage; The finite element

Abstract

The main shaft of shot blasting machine mainly uses cylindrical roller bearing as the supporting part. The influence of stress, strain and temperature on bearing damage was studied by thermal-structural coupling analysis of the bearing through finite element simulation. The causes and main damage forms of bearing surface were verified by super depth of field observation and finite element analysis. It is found that there exists pyramidal strain in the contact area between inner and outer raceway and roller, and its distribution form is continuous point distribution. The stress concentration is mainly distributed in the contact area between the roller face and the retaining edge, and the roller temperature is more concentrated in the area near the end face. The maximum length and depth of spalling pit on racetrack surface were and respectively. The maximum width and depth of the scratches on the roller surface are and , and the damage degree of the roller surface is not uniform. The thermo-structural coupling analysis is used to simulate the running state of bearings, which is of guiding significance to analyze the failure forms of bearings and improve the service life of bearings.

Introduction

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Cylindrical roller bearings are widely used in manufacturing preparation because of their large bearing capacity and high speed and heavy load conditions[1-2]. Through modern finite element analysis, the bearing structure is constantly optimized, and the service life and performance of bearings are constantly improved[3]. Bearing performance is directly related to the use of the spindle system, and has a direct impact on the stability and reliability of the whole equipment[4]. Therefore, as far as possible to reduce bearing damage in the process of use, improve the service life has been the focus of research in the industry.

The main shaft of the shot blasting machine uses a pair of cylindrical roller bearings as the main support. Problems such as skidding, heating up and vibration will occur during the use of the main bearing, which will inevitably cause some damage or even failure to the bearing. Studies have found that about of bearing failures are caused by fatigue. Many scholars at home and abroad have conducted extensive studies on bearing dynamics and statics, and conducted thermal-structural coupling analysis of bearings through finite element to explore the causes of bearing failures[5].

In this paper, the main shaft cylindrical roller bearing NU2218E of shot blasting machine is taken as the research object. The finite element simulation analysis of the bearing was carried out according to the working condition to study the influence of the bearing heat and force on the damage under the working condition.

Analysis of Motion State of Cylindrical Roller Bearing

The original version of this paper is available on https://www.scientific.net/EI.4.1.pdf

The research on the motion state of cylindrical roller bearing requires the hypothesis analysis of the motion mode of roller. It is assumed that the bearing roller only has pure rolling in the motion
process and has good contact with the inner and outer rings, and the contact mode is linear contact. Bearing inner ring angular velocity is ; The rotational angular velocity of the roller is ; The roller rotates at an angular velocity of , which is opposite to the angular velocity of revolution. F is the radial load of the bearing, is the radial load of the roller, and is the reaction force of the radial force F . Figure 1a shows the motion state of the bearing.

Fig. 1 Bearing motion state diagram (a) and thermal structure model (b)

Fig. 1. Bearing motion state diagram (a) and thermal structure model (b)

During the installation process, there is a certain gap between the roller and the inner ring. After loading, the contact point of the lower roller and the inner ring is and the contact point of the outer ring is . The contact point of the upper roller is . When the roller moves to the bottom of the bearing chamber, it receives the maximum radial force . According to formula 1.1, the maximum radial force on the roller can be calculated

Where: F -- radial force received by the bearing; Z -- number of rollers. Formula 1.1 is the contact force of bearing line.

Under the action of radial force reaction force Frcosn , the roller is subjected to tangential component force, and the roller rotation line speed does not match the inner ring line speed. The bearing roller is filled with grease between the inner and outer rings. If is too large, the thickness of the oil film will decrease and the friction force will increase. Bearing heat mainly comes from the heating of roller stirring grease and the collision and friction between roller and inner and outer raceway. The heat generated by the bearing forms thermal convection through the grease, and the heat is transmitted to the inner and outer rings for heat conduction and outward diffusion. Figure 1b is the bearing thermal structure model diagram.

Bearing friction power loss is converted into heat, and power loss mainly depends on friction torque. Part of the friction torque is determined by the material and speed

Where: -- bearing section circle diameter, mm ; -- Coefficient related to bearing type and lubrication mode, take -- Bearing speed, take ; v -- Kinematic viscosity of lubricant, take .

The other part of the friction torque is determined by the load

Where: -- coefficient related to load, -- Load on bearing, N .
The total friction torque can be simplified as

The power loss caused by friction heat can be calculated by the friction torque

Through calculation, the power loss of single roller is 68.14 W .

Finite Element Analysis

The original version of this paper is available on https://www.scientific.net/EI.4.1.pdf

3D modeling. The three-dimensional model of NU2218E cylindrical roller bearing was established by using finite element software. The specific design parameters of the bearing were shown in Figurre 2, and the other modeling parameters were obtained by measuring the entity. In the modeling process, chamfering, fillet and cage are omitted to simplify the model calculation. Three-dimensional bearing model is shown in Figurre 2.

Fig. 2 NU2218E 3D model and design parameters

Fig. 2. NU2218E 3D model and design parameters

Define unit and material parameters. Bearing steel has high hardness and large elastic modulus. Common grades are GCr6, GCr15, G20CrMo and so on. The internal diffusion of heat in the bearing is complicated. Solid unit Solid226 and surface effect unit Surf152 are selected for structural thermal analysis. Solid226 is a 20 -node, 6 -degree-of-freedom solid unit with hybrid analysis capabilities for elastic deformation analysis of incompressible materials. The Surf152 is mainly used for various 3D thermal analysis and can be superimposed on any thermal cell surface. GCr15 was selected as the bearing material for analysis, and the material parameters are shown in Table 1.

Table 1 GCr15 material parameter table

Parameter | Numerical value
Temperature/°C | 20 | 100 | 200 | 300
Density/ (kg· m³) | 7791.8 | 7766.7 | 7737.8 | 7706.8
Modulus of elasticity/ MPa | 2.13×10⁵ | 2.07×10⁵ | 2.01×10⁵ | 1.95×10⁵
Poisson's ratio | 0.3 | 0.3 | 0.3 | 0.3
Thermal conductivity/ (W·m⁻¹·K⁻¹) | 35.62 | 39.05 | 39.36 | 37.76
Specific heat/ (J·kg⁻¹·K⁻¹) | 477.63 | 491.46 | 505.12 | 525.46
Coefficient of thermal expansion/ K⁻¹ | 10.23×10⁻⁶ | 10.72×10⁻⁶ | 11.54×10⁻⁶ | 12.11×10⁻⁶

Meshing, establishing contact pairs and surface effect elements. The outer ring, roller and inner ring of the bearing are meshed respectively. The grid length is set to 0.0024 m , and the grid shape is triangular, which can be meshed freely. A total of 281302 units and 337094 nodes were established. Ten mutually independent contact working surfaces, including inner surface, outer surface, inner raceway, outer raceway, front and rear guard edge, front and rear end face of roller, upper and lower surface of roller, were selected, and the surface effect units were established successively. A total of 10 surface effect units were established. Contact pairs were selected, and contact pairs were established for outer raceway-outer surface of roller, inner raceway-inner surface of roller and front and rear fenders -bearing end face. The contact mode was face-surface contact, and a total of 4 contact pairs were established. Figure 3 is a schematic diagram of meshing, surface effect unit and contact pair.

Fig. 3 Schematic diagram of meshing, surface effect unit and contact pair

Fig. 3. Schematic diagram of meshing, surface effect unit and contact pair

Apply boundary conditions. Bearing is complicated in the actual running process, so it needs to be simplified and analyzed as an ideal model. The rotation speed of the shot blasting machine will be affected by the load, so the spindle angular speed is . When the spindle is running at high speed, the main load comes from the rotating shaft and the impeller. It is assumed that the spindle is subjected to a fixed load F in the normal operation, with a vertical downward direction and a magnitude of 1000 N . The initial temperature applied to the outermost ring is , the initial temperature applied to the innermost ring is , and the initial temperature increases successively from the outermost ring to the innermost ring. In the cylindrical coordinate system, the bearing outer ring is stationary, and all nodes on the outer surface of the bearing outer ring are fully constrained. The inner ring of the bearing can only rotate axially due to the limitation of the main shaft, and the X -axis and Z -axis constraints are imposed on the nodes on the inner surface of the inner ring. The roller is restricted by the inner and outer ring and the guard edge can only rotate and revolution. The X -axis constraint is imposed on the circular surface node of the roller, and the Z -axis constraint is imposed on the end node of the roller. Figure 4 is a schematic diagram of bearing boundary conditions.

Fig. 4 Schematic diagram of boundary conditions

Fig. 4. Schematic diagram of boundary conditions

Analysis of Simulation Results

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Stress analysis. The results show that the surface of the outer raceway, roller and inner raceway has many spot-like protrusions, the strain of outer ring retaining edge is outward, and the strain of roller face is different degree. Because the force acts vertically on the vertical direction of the contact line between the roller and the raceway, the surface of the contact line is subjected to compression deformation, and the material around the contact line is subjected to a certain degree of tensile stress, resulting in tensile deformation[7]. At the same time, due to the temperature rise, the bearing parts produce thermal expansion, which further increases the contact stress between the roller and the outer raceway, and adversely affects the oil film between the roller and the raceway in the operation process. As a result, the force element of the outer raceway is subjected to repeated tensile/compressive stress, and the surface is subjected to periodic tensile/compressive elastic deformation, and a spot-like strain area is formed. Figure 5 is the bearing strain cloud diagram.

Fig. 5 Cloud diagram of bearing strain

Fig. 5. Cloud diagram of bearing strain

The stress at the outer rim of the ring is distributed at intervals, the stress at both ends of the roller is more concentrated, and the stress on the inner pipe surface is arranged neatly and distributed in strips. Roller high-speed operation produces a lot of friction heat, resulting in the expansion of the roller heat caused by extrusion of the retaining edge. At the same time, the "edge effect" leads to the stress concentration at both ends of the roller, which forces the elastic deformation at both ends of the roller to be larger than the middle area[8]. The above reasons cause stress concentration in the contact area between the retaining edge and the roller face. It can be seen from Figure 6 that the deformation of roller end face and retaining edge is not uniform, and the contact state is complex surface contact, which aggravates the friction and wear of working surface. Figure 6 is the bearing stress cloud diagram.

Fig. 6 Cloud diagram of bearing stress

Fig. 6. Cloud diagram of bearing stress

Temperature field analysis. The bearing temperature gradually decreases from the outer surface to the inner surface. The highest roller temperature is the contact area between the roller and the outer raceway, and the temperature at both ends of the roller is higher than the middle area. The temperature gradient between the outer raceway and the roller, and between the roller and the raceway is large, which is caused by the different movement speed of grease in the bearing cavity. With the increasing bearing temperature and height, the viscosity coefficient of grease decreases continuously, and the effective bearing oil layer cannot be formed between roller and raceway, resulting in the increase of friction between roller and raceway. The load of the main shaft of the shot blasting machine is not stable in the working process, which leads to the bearing will be affected by impact and vibration in the operation process, and skid and rub injury occurs[9-10]. The temperature of contact area between roller and raceway increases sharply due to skid and scratch injury, and the temperature rise of outer raceway is obviously higher than that of inner raceway. Figure 7 is the bearing temperature cloud diagram.

Fig. 7 Cloud chart of bearing temperature

Fig. 7. Cloud chart of bearing temperature

Damage Verification

The original version of this paper is available on https://www.scientific.net/EI.4.1.pdf

Cylindrical roller bearings include outer ring, inner ring, roller and cage. Different degrees of friction will occur between roller and inner raceway, roller and outer raceway, roller end face and rim in the process of bearing operation.

Compared with the undamaged raceway surface, the spalling pits of different sizes are not evenly distributed on the damaged raceway surface, and the spalling area is mainly concentrated in the middle of the raceway, which is consistent with the regular strain band composed of point strains on the raceway surface outside the strain cloud map. In some areas of the outer ring retaining edge, there are scratches distributed along the direction of roller movement. As can be seen from the stress-strain cloud diagram, the strain on the retaining edge is large along the symmetrical direction of the central axis, and the indirect stress is concentrated along the circumferential direction of the bearing. The two ends of the cylindrical roller are trimmed during the manufacturing process to increase the fatigue resistance of the roller[3]. Roller at both ends of the arc slope (bright belt) there is about 18 mm wide wear area, distribution of irregular ribbons and scratches. Comparing the cloud images, it is found that there are high temperature and stress concentration in the area with severe roller scratches. Figure 8 Comparison of damage of roller bearings.

Fig. 8 Damage comparison diagram of various parts of roller bearing

Fig. 8. Damage comparison diagram of various parts of roller bearing

Through the ultra depth of field equipment can intuitively observe the damage, respectively at the outer raceway spalling pit and roller surface observation. Select the concave area of the outer raceway for super depth of field observation, it can observe the obvious spalling pit, the location of the concave pit and the surrounding area have an obvious height difference, and the concave pit is relatively gentle, the edge of the irregular fracture zone. The section was observed in direction of the pit. The length of the section was , and the maximum depth was . Figure 9 is a super depth of field view of the outer raceway surface.

Fig. 9 Observation of super depth of field on inner raceway surface

Fig. 9. Observation of super depth of field on inner raceway surface

Select the roller with color band for observation. There is obvious color difference on the roller surface, and there are many peeling pits and scratches. Two parallel lines M and N were selected on the adjacent position of the roller surface. The maximum width and depth of the scratches on the selected position were and respectively. And M, N two lines have obvious height difference, which indicates that the roller surface damage degree is not uniform, there are different degrees of pits and different depths of scratch. Figure 10 shows super depth of field observation of roller surface.

Fig. 10 Observation of super depth of field on roller surface

Fig. 10. Observation of super depth of field on roller surface

Bearing in the use of poor heat dissipation, temperature difference between the two parts is too large, large thermal deformation and local stress concentration will produce scratches, scratches and overheating damage, with the increase of the use of time, damage intensification, slowly developed into burns, scratches and peeling[11]. There are many spalling pits on the surface of the raceway due to the alternating load in the process of use, and the main damage form is fatigue pitting. After the roller deformation under load and thermal expansion deformation, there is a large pressure on the edge of the guard, resulting in the stress concentration of the edge of the guard, a large strain, the main form of damage is scratch. Under the working environment of , the viscosity coefficient of grease decreases, and the oil film with effective bearing capacity cannot be formed. Its lubrication and protection effect is weakened, leading to direct contact and relative sliding between roller and inner and outer raceway. At the same time, the instantaneous temperature of roller surface is too high due to the flash temperature effect caused by skidding, and the damage form is mainly burn, scratch and pitting.

Summary

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Due to the limitation of finite element analysis, the mesh is divided by triangles freely and the solid element is a hexahedron model. Because the contact area between roller and raceway is very small, great contact stress and strain are produced, which leads to some errors in the analysis results. Applying boundary conditions closer to actual working conditions and reducing analysis errors will be the next key task. Through the structural thermal analysis of the cylindrical roller bearing NU2218E, the following conclusions are drawn:
(1)Bearing in the process of use will produce high temperature, roller and outer raceway contact area temperature is greater than the inner raceway contact area, which is consistent with the fact that the damage degree of the outer ring is greater than the inner ring.
(2)The inner and outer raceway surfaces are subjected to periodic tension and compression stress, and the strain of each part of the bearing appears in the finite element analysis similar to pyramid shape.
(3)There are obvious spalling pits on the outer ring surface. There are scratches and spalling pits on the surface of the roller, and the scratches are not consistent in the circumference of the roller depth; The edge of the guard is severely scratched under high stress.

References

The original version of this paper is available on https://www.scientific.net/EI.4.1.pdf

Jin Qianchong, Zhang Qing, Luo Jun, Xie Xiangyu, Xu Jin. Bearing, 2019 (12): 39-43

Yang Fan, Xu Kefeng. Harbin bearing, 2020,41 (03): 19-22

Xu Mingqi, Zheng Hongwei, Qu Qiong. Bearing, 2014 (08): 28-31

Chen Jiaqing, Zhang Pei, Xu Linlin. Journal of Beijing Institute of petrochemical technology, 2001 (01): 32-38

Jia Dongsheng, Zhang min. Journal of Zhejiang Vocational and Technical College of industry and commerce, 2010,9 (04): 82-83 + 88

Song Nan. Jilin University, 2017

Liu Jinchao, Wang Chengguo. China Railway Science, 2002 (02): 86-94

Chen Jiaqing, Zhang Pei, Xu Linlin. Journal of Beijing Institute of petrochemical technology, 2001 (01): 32-38

Fowell M, Ioannides S, Kadiric A. Tribology Transactions, 2014, 57(3): 472-488.

Li J, Chen W, Xie Y. Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology, 2014, 228(10): 1036-1046.

Li Haiqi, Yang Xu. Fan technology, 2008 (06): 78-80

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The original version of this eBook is available on https://www.scientific.net/book/engineering-innovations-vol-4/978-3-0364-1008-1

Engineering Innovations, ISSN: 2813-1002, Vol. 4, pp 9-14

doi: 10.4028/p-23x9u4

© 2023 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2022-04-28

Revised:
2022-07-01

Accepted:
2022-07-08

Online:
2023-02-15

Vibrational analysis of Mini - Unmanned Aerial Vehicles Due to Gun Recoil

The original version of this paper is available on https://www.scientific.net/EI.4.9.pdf

N. Rino Nelson and P.M. Siva

Department of Mechanical Engineering, Indian Institute of Information Technology Design and Manufacturing Kancheepuram, Chennai, 600127, India

rino@iiitdm.ac.in, mds20m007@iiitdm.ac.in,

Keywords: Resonance; pressure canister; recoil; vibration; mini-UAVs.

Abstract

In 21st century, battlefields are being occupied by Artificial Intelligence (AI) controlled machines and one of its kind is mini-unmanned aerial vehicles. Upon arming the mini-UAVs, the load distribution and characterizing the vibrational behavior are important for its safe operation. Usually, the gun recoil force gets transferred to the platform of the mini-UAV, leading to instability or failure of the platform along with the gun. Mini-UAVs being too small don't have the space to set the conventional recoil reduction mechanism. So, it is important to design a mechanism or alternative propellant for achieving the equivalent explosive force instead of TNT. Also, the influence of explosion on the vibration characteristics of the mini-UAV is studied. The high-pressure gas is found as the best alternative to TNT material, for reducing the deflection produced. This work primarily concentrates on determining the deflection and frequency induced in mini-UAVs. By using a pressure canister arrangement, the vibration characteristics under recoil can be improved.

Introduction

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Nowadays in battlefields, aircraft is being replaced by unmanned aerial vehicles (UAV) for dog fight, reconnaissance and air-superiority. Even though, the UAVs have the capability to carry large range of ammunition, mini-UAVs were developed for specific operations to make them economically viable. But, its capability of ammunition needs to be improved in mini-UAVs. Upon ammunition, the rearward (recoil) force from the machine gun will be high, which can lead to destruction or failure of the entire unit. The recoil force of the gun gets transferred to the base platform of the UAV, which leads to instability or failure of base platform along with the gun [1]. Usually, UAVs have special recoil reduction mechanism to keep the platform in stable position. To avoid failure, dashpot-spring mechanism is commonly used to absorb the recoil force and bring the cartridge to its original position to fire the next bullet. Here, the recoil frequency depends on the damping coefficient of the dashpot, which is maintained greater than 0.5 for machine guns. Mini-UAVs being too small don't have the space to set the conventional recoil reduction mechanism, making it difficult for ammunition.

In literature, more importance is given to the structural analysis of mini-UAV. This always leads to finding a suitable material, so that the stress lies within the failure limit [2, 3]. With recent advancement in topology and topography optimization, the frame behavior can be analyzed and costeffective alternatives are proposed [4]. The position algorithm can be used to find the natural frequency at every nodal point in the frame, which helps to position the sensor locations. Most of the unbalance in the mini-UAV occurs due to the deviation in total weight acting on the center of gravity of the mini-UAV, leading to calibration errors. To reduce this error, weight distribution algorithm employed in aircraft is commonly used.

Now with the advent of swarm drone technology, the need for arming the mini-UAVs is increasing. So, it is important to design a mechanism within the limited space, which transfers less vibration to the base platform. This paper concentrates on determining the vibrational characteristics of miniUAVs and different methodology of recoil reduction for improving the stability of the base platform. When a gun is attached to the mini-UAV, the recoil force causes extra vibrations [5] and shifts the natural frequency of the mini-UAV. The common approach is to use special dampers like MR dampers [6], but these are suitable only for large caliber guns like Howitzers and Anti-aircraft guns. Small caliber guns can only be used in mini-UAV due to the weight constraint. But, small caliber
guns like pistols produce more recoil force and has less space to accommodate any special damper arrangements. The suitable approach to solve this problem is to use rarefaction gun [7] and different propellant. A unique way is to use pressure canister mechanism, which can produce less vibration and recoil force compared to the normal chemical propellant. The internal ballistics of the gun is analyzed using special codes to calculate the peak pressure and muzzle velocity [8]. But with pressure canister mechanism, we can eliminate the use of complex ballistics code and common Navier stokes equations are solved.

Another area which needs more attention is to reduce the transmissibility ratio between gun and mini-UAV, so that the vibrations reaching the frame can be reduced. If the mini-UAVs are directly in contact with the gun, the transmissibility ratio will be close to 1 . The researchers have focused more on the effect of structural methods to reduce the vibration, but not much importance has been given to the use of alternative propellants. Once such method is pressure cartridge or canister method in which pressure will be used to propel the projectile. The effect of using pressure canister as an alternate propellant is investigated in this work.

Numerical Analysis

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First step in building a numerical model for internal ballistics calculation is determining the dependent and independent parameters, along with the theories linking these parameters. The most important parameters include the muzzle velocity and barrel pressure . There exists a variety of analytical approach to describe the interior ballistic process. They are chemical equilibrium, closed form formulations, zero order analytical method and lumped parameter methods. Among various methods [9, 10], Corner's method and Coppock's method belonging to closed form formulation, yield better results compared to other methods. The main benefits of these approaches include the use of dimensionless ballistic parameters and usefulness in testing the convergence and accuracy of developed numerical model [10]. The results from ANSYS are compared with both the analytical methods. The flowchart describing the methodology used in this work is shown in Fig. 1.

Table 1. Input parameters of the barrel

Input Parameter | Value
Bore Diameter | 18.52 mm
Barrel Length | 457.2 mm
Projectile mass | 0.044 kg
Propellant type | Cylindrical
Propellant diameter | 1.575 mm
Adiabatic flame temperature | 3390 K
Covolume | 0.890 cm³/g
Chamber volume | 983200 mm³

Fig. 1 Flowchart of Methodology

Fig. 1. Flowchart of Methodology

Corner's Method. Corner's method is one of the closed form solutions used for internal ballistics calculation which helps in determining the maximum breach pressure and muzzle velocity . The input parameters needed are projectile diameter, burn rate coefficient, gas co-volume, adiabatic flame temperature, barrel length and diameter. The input parameters for barrel and projectile are listed in Table 1. The first output variable in Corner's method is central ballistics parameter (M), given by Eq. 1. The breach pressure and muzzle velocity are calculated using Eq. 2 and 3.

Coppock's Method. Coppock's method is also a closed form approach. Coppock's method is more accurate than Corner's method. The assumptions made are powder mass burn rate is proportional to the chamber pressure and surface area of the powder. The pressure gradient is assumed as constant for a charge weight and projectile weight ratio. The maximum pressure and muzzle velocity ( ) are given by Eq. 4 and 5.

ANSYS Model Setup

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Barrel Dimensions and Properties. Gun barrel dimensions have a major contribution towards muzzle velocity [1-3] i.e., longer barrel gives more time for the propellant to expand to its threshold limit which leads to maximum muzzle velocity. But longer barrel causes heaviness and instability making the mini-UAVs to lose control during loitering mode. Therefore, usage of short barrel is recommended in mini-UAVs to fly in stable condition. To analyze the effect of short barrel, a standard length of 116 mm is chosen as shown in Fig. 2.

Fig. 2 Barrel model in ANSYS

Fig. 2. Barrel model in ANSYS

Projectile Dimensions and Properties. The use of small barrel length leads to more vibrations with propellant expansion. To reduce this vibration, we should choose bullet having small diameter propellant with medium peak pressure. This small diameter also leads to reduction in weight, so more ammo can be carried. Based on the above conditions, a bullet with 9 mm diameter is chosen for analysis (Figs. 3 to 6). To withstand the temperature produced during burning of propellant, the bullet
material is chosen as lead. The cartridge material is chosen as copper alloy to have a limited deformation, since the whole explosion needs to be confined to move only in one direction.

Fig. 3 Bullet model

Fig. 3. Bullet model

Fig. 5 Explosive model

Fig. 5. Explosive model

Fig. 4 Cartridge model

Fig. 4. Cartridge model

Fig. 6 Full bullet model

Fig. 6. Full bullet model

Results and Discussion

The original version of this paper is available on https://www.scientific.net/EI.4.9.pdf

Analytical vs Numerical. The simulation is performed using ANSYS software. The muzzle velocity and maximum breach pressure values are obtained as output from the simulation. The results obtained from analytical methods (i.e) Corner's and Coppock's methods are compared with values obtained from numerical simulation.

Table 2. Comparison of values from analytical and numerical methods

Method | Muzzle velocity [m/s] | Max breach pressure [MPa]
Corner's Method | 815.26 | 225.38
Coppock's Method | 826.75 | 205.33
ANSYS | 800.78 | 224.37

Deflection comparison. The barrel deflects due to the burst of primer and burning of propellant, which also causes the barrel to vibrate. To reduce the induced vibration, the pressure canister is proposed. This mechanism has no propellant, but the high-pressure air released causes the projectile to move. The deflection of the barrel with pressure canister is comparatively low as shown in Fig. 7. For TNT, the primer burst leads to deflection and thereby causing initial peak in Fig. 7. The second peak corresponds to the propellant burst, causing the expansion of gases and the last peak corresponds to the projectile leaving the barrel. In canister curve, the first peak corresponds to the initial burst of the bubble, which has high pressure air. The second peak corresponds to the projectile leaving the barrel. The curve clearly shows that the canister has reduced the deflection when compared to the use of TNT. Since the deflection is reduced, it will lead to the reduction in vibration along the lateral direction significantly.

TNT vs Pressure Canister. Free vibration analysis helps in determining the natural frequency /resonance frequency of the model. The model is preferred to vibrate with the limit but when it exceeds the limit, the failure happens. In addition, the effect of bullet burst also contributes to vibration causing failure in many cases. To avoid this condition, dampers and vibration absorbers are used suppressing the frequency below the threshold level. With the use of pressure canister, the need
of damper is less, since the force imparted is lower than TNT. But, the force is transient and not static in nature. So, the dynamic results of both are compared in the Table 3.

Fig. 7 Comparison of deflection vs time for TNT and Canister

Fig. 7. Comparison of deflection vs time for TNT and Canister

The values show that the vibration due to the pressure canister is lower compared to TNT. The fourth frequency of canister is higher than the TNT, because of the muzzle pressure breaker in TNT and the absence of the same in pressure canister. Overall, the force produced is less for high pressure gas with time. But for TNT, the expansion of the gas increases the force until the projectile moves and then decreases slowly compared to the high-pressure gas stored in the canister.

Table 3. Frequency due to TNT and canister

| Frequency of TNT [Hz] | Frequency of canister [Hz]
1 | 60.25 | 56.82
2 | 167.25 | 158.25
3 | 304.50 | 298.60
4 | 448.25 | 456.28

Conclusions

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Based on the present study, feasibility of using alternate propellant for projectile motion is studied and the following conclusions are derived,

The results of deflection and vibrational frequency show that the frequency is lower with the use of the high-pressure canister than the TNT material. There are also propellants which have lower blast peak pressure reducing the deflection.

The frequency of vibrations still be same and necessitates the use of damper, which increases the weight.

Though pressure canister is better from vibration perspective, it has low range of fire and low projectile velocity upon reaching the target.

To determine a suitable frame for carrying the gun and withstand the vibrations, topology optimization needs to be done. This further helps in making the mini-UAV more stable for the recoil.

Nomenclature

The original version of this paper is available on https://www.scientific.net/EI.4.9.pdf

A Area of bore
D Web fraction
f Mass fraction
Barrel length

References

The original version of this paper is available on https://www.scientific.net/EI.4.9.pdf

M. Urdea, Stress and vibration analysis of a drone, IOP Conference Series: Materials Science and Engineering. 1009 (2021) 012059.

M. Kim, J. Kim, Y. Byun, J. Kim, B. Kang, Study on Analysis of Vibration Characteristics and Modal Test for a Quad-Rotor Drone, Journal of the Korean Society for Precision Engineering. 33 (2016) 707-714.

J. Verbeke, S. Debruyne, Vibration analysis of a UAV multi-rotor frame, International conference on Noise and vibration Engineering. (2016).

H. Hua, Z. Liao, J. Song, Vibration reduction and firing accuracy improvement by natural frequency optimization of a machine gun system, Journal of Mechanical Science and Technology. 29 (2015) 3635-3643.

Y. Li, S. Wang, J. An, J. Zhang, Research on low recoil launch technology of small UAV with small arms mounted on it based on floating principle, Journal of Physics: Conference Series. 1314 (2019) 012081.

Z.T. Fan, R.L. Wang, T. Li, Simulation Analysis of Firing Dynamics on a New Heavy Machine Gun. Applied Mechanics and Materials. 574 (2014) 32-35.

E. Chaturvedi, Numerical investigation of dynamic interaction with projectile and harmonic behaviour for T-finned machine gun barrels. 16 (2) (2019) 460-469.

H. Hua, M. Qiu, Z. Liao, Dynamic analysis of an axially moving beam subject to inner pressure using finite element method. Journal of Mechanical Science and Technology. 31 (2017) 26632670.

J. Corner, Theory of the Interior Ballistics of Guns. New York, New York, United States of America: John Wiley and Sons Inc., 1950.

R.T. Miner. Computational interior ballistics modeling. M.S. Thesis. UNM Digital Repository, Mexico, 2013.

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Engineering Innovations, ISSN: 2813-1002, Vol. 4, pp 15-20

doi: 10.4028/p-0l1ejd

© 2023 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2022-03-25

Revised:
2022-06-16

Accepted:
2022-07-08

Online:
2023-02-15

Artificial Intelligence Predictions Effect of Loading Rate, Crack Width and Crack Length Ratio on Mode I Fracture Toughness of PMMA

The original version of this paper is available on https://www.scientific.net/EI.4.15.pdf

Attasit Wiangkham , Prasert Aengchuan , Atthaphon Ariyarit

School of Manufacturing Engineering, Institute of Engineering, Suranaree University of Technology, Muang, Nakhon Ratchasima 30000, Thailand

School of Mechanical Engineering, Institute of Engineering, Suranaree University of Technology, Muang, Nakhon Ratchasima 30000, Thailand

attasitwk@gmail.com, prasert.a@sut.ac.th, ariyarit@sut.ac.th

Abstract

Present, artificial intelligence methods play a huge role in solving complex engineering problems such as the fracture toughness of materials, which is one of the parameters to be considered for engineering design. Fracture toughness tests can be prepared materials and test configured in a variety of ways, resulting in different fracture toughness depending on the preparation method. In this study, fracture toughness of PMMA under the effect of loading rate is one of the testing configs that can be adjusted according to the actual load characteristics of the material and the crack geometry (crack width and crack length ratio) according to crack preparation to test specimens and the effect of these factors was predicted with generalized regression neural network (GRNN) and Gaussian processes regression (GPR) models which are one of the artificial intelligence models, compared to traditional fracture toughness predictions. The results showed that artificial intelligence prediction was able to more accurately predict the effect of the factors studied on the fracture toughness of PMMA compared to the traditional fracture toughness prediction.

Introduction

The original version of this paper is available on https://www.scientific.net/EI.4.15.pdf

Present, artificial intelligence methods play a huge role in solving complex engineering problems, such the fracture toughness of materials, which is one of the parameters to be considered for engineering design. Fracture toughness is a parameter in fracture mechanics that studies the behavior of a material where cracks or discontinuities occur and are subjected to external loads. It is commonly known that the fracture toughness values of materials depends on the type of material, loading characteristics, and geometry of the testing specimen [1]. To know how the above factors affect fracture toughness most of the general methods have to be tested on real materials which will cause quite a lot of expenses. For this reason, this study aims to create an equation that can be used to predict the effect of such factors on the fracture toughness of materials using artificial intelligence methods (AI) that are popular in the materials field today [2]. The widely used AI algorithm such as generalized regression neural network [3] and Gaussian processes regression [4] were selected to a created a prediction model based on actual fracture toughness obtained from experiments. The fracture toughness testing, AI prediction modeling, and results of AI model prediction performance compared to traditional prediction were described in the next section of this study.

Mode I Fracture Toughness Testing

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In this study, the effect of loading rate and crack geometry (crack width and ratio of crack length to specimen width) on mode I fracture toughness of poly(methyl methacrylate) sheet or PMMA sheet which is widely used in lab-scale experiments was investigated. The single edge notch with a threepoint bending specimen was used to tests mode I fracture toughness (Fig. 1). To determine the effect of crack geometry, the crack width of the specimen is defined as 1,3 , and 5 mm respectively, and the ratio of crack length to specimen width is defined as , and 0.7 respectively. The specimens are prepared by laser cutting. Fracture toughness testing was performed on Lloyd universal testing
machine LD series at loading rates , and . The testing sequence was generated using a general full factorial experiment design with 3 replicates for each condition. After testing the mode I fracture toughness can be calculated according to Eq. (1)

Fig. 1 Dimension of fracture toughness specimen

Fig. 1. Dimension of fracture toughness specimen

where is the stress intensity factor that demonstrated fracture toughness of this study, is the maximum load applied to specimen, is the width of specimen, is the thickness of specimen, is the initial crack length and is the mode I fracture toughness geometry factors that depended on the ratio of crack length to specimen width ( ), the ratio of span length to specimen width ( ) and crack width ( ) which can be calculated using finite element analysis (FEA) which at crack width 1 mm are equal to (1.589, 2.090, 4.068), 3 mm are equal to (1.537, 1.997, 4.027), and 5 mm are equal to (P.S.: number in the bracket was indicated at equal to , and 0.7 respectively).

Prediction of mode I fracture toughness

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Traditional fracture toughness prediction

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Generally, when considering fracture toughness, it was found fracture toughness arises from the relationship of stress that occurred on the material. With this relationship, many researchers have come up with equations for predicting fracture toughness, known as fracture criteria. In this study, the widely used average strain energy density criterion (ASED) was employed to prediction fracture toughness. The ASED criterion was predicted based on the materials will be fractured when the average strain energy density around the crack tip over a control volume reaches critical strain energy ( ) which depended on materials and notch properties. The prediction equation of ASED criterion was described according to Eq. (2)

where and are critical strain energy density obtained from mode I and II fracture respectively which can be calculated according to Eq. (3) and is mode I and II strain energy density generated from load applied to specimen respectively that can be calculated following Eq. (4) (When considering mode I, assumed which obtained from mode II is equal to 0 )

where is tensile strength of material (MPa) that equal to , and 74.15 at each loading rate and is the modulus of elasticity (GPa) that equal to , and 3.13 at each loading rate.

where is equal to 0.1186 [5], is mode I Williams' eigenvalues equal to 0.5 , and is mode I control volume radius[6].

Artificial intelligence method

The original version of this paper is available on https://www.scientific.net/EI.4.15.pdf

Data preparation and model performance evaluation

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Data preparation was the process that affected to prediction performance of the artificial intelligence (AI) model. The data preparation was started by reducing the different scale of input factors with normalization technique to range 0 to 1 . To avoid bias occurring in train and test dataset selection process, the K-Fold cross-validation method was employed for selection. The loading rate , crack width ( ), and crack length ratio ( ) was selected as input factors of AI models, and fracture toughness ( ) was selected as output or target of models. All AI model was generated in MATLAB programming. The model performance evaluation for both traditional and AI models has employed the common performance metric in regression problems such as coefficient of determination ( ) and mean absolute percentage error (MAPE) which indicated the difference between prediction and actual values, both and MAPE equation described at ref [7].

Generalized regression neural network (GRNN)

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The generalized regression neural network (GRNN) is one of the neural network models. The learning process of GRNN is a feedforward training type that belongs to the radial basis model. The architecture of GRNN was shown in Fig. 2 and brief was described as follows:

Fig. 2 Architecture of generalized regression neural network (GRNN)

Fig. 2. Architecture of generalized regression neural network (GRNN)

Input layer: In this layer, the inputs data are introduced into the model learning process. The number of neurons in this layer is according to the dimension of the input vector.
Pattern layer: Data of the input layer were passed into this layer and transformed by the Gaussian kernel function. The number of neurons in this layer is according to the number of the training data.
Summation layer: The weight was summarized in this layer. The is neurons that officiate the summation of weight and output of training data while is officiating the summation of weight only. Output layer: The prediction results of model ( ) were generated in this layer which was calculated from the ratio of and from the previous layer. Additional information on GRNN can be found at ref. [4]

Gaussian process regression (GPR)

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Gaussian processes regression (GPR) is a generic supervised learning method designed to solve small dataset problems. The GPR models are nonparametric covariance or kernel-based probabilistic models that combine prior process and sampled training data to infer posteriori processes as interpolation results and brief was described as follows:

Applied the Gaussian Processes method (GP) into the relationship function of input ( ) and output or , the GP of defines a priori over functions, which can be converted into a posteriori over functions once some data is obtained. The Gaussian process is indicated as follows:

where is the mean function that depicts the anticipated value of at the input and is the covariance function or kernel function of the measured confidence level for . The prediction equation of GPR shown in Eq. (6)

where is the corresponding output of input is the Gaussian distribution, is the mean of the Gaussian distribution, and is the variance of the Gaussian distribution. Then known as the prediction values

Results and Discussion

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The fracture toughness affected by loading rate and crack geometry of PMMA were shown in the form of average values in Fig. 3(a) which result shows a tendency for fracture toughness to decrease as the loading rate increases. When considering the effect of the crack geometry, the same behavior is observed as the loading rate at which the fracture toughness decreases when the crack geometry, both the crack width and the crack length ratio are increase. Evidence of the effect of these three factors is evident when considering the Pareto chart of standardized effect obtained from ANOVA (Fig. 3(b))

Fig. 3 Fracture toughness results (a) average fracture toughness results for each condition (b) Pareto chart of standardized effects on at significant level 0.05

Fig. 3. Fracture toughness results (a) average fracture toughness results for each condition (b) Pareto chart of standardized effects on at significant level 0.05

The results of the predictions with the ASED criteria separated by loading rate are shown in Fig. 4, it was found to be rather inaccurate compared to the experimental results. The ASED criteria had MAPE values is , and according to each loading rate. While compared to AI models (Fig. 5), it was found to have significantly higher predictive performance. The GRNN model had and MAPE values equal to 0.904 and respectively and the GPR model had and MAPE values equal to 0.909 and respectively. When considering MAPE values according to

Lewis's interpretation [8] that was found both AI models are high prediction performance (<10%) while the ASED criterion found a good or reasonable prediction ( ).

Fig. 4 Prediction fracture toughness from ASED criterion at a various loading rate

Fig. 4. Prediction fracture toughness from ASED criterion at a various loading rate

Fig. 5 Prediction results of artificial intelligence model

Fig. 5. Prediction results of artificial intelligence model

Conclusion

The original version of this paper is available on https://www.scientific.net/EI.4.15.pdf

The objective of this research was aim to prediction effect of loading rate and crack geometry on mode I fracture toughness of PMMA by generalized regression neural network (GRNN) model and Gaussian processes regression (GPR) model which not presented before. The results of the operation can be summarized as follows:

The loading rate and crack geometry (crack width and crack length ratio) has affected to mode I fracture toughness of PMMA, and the loading rate has the lowest affected compared to crack geometry.

The ASED criteria had inaccuracy in the case of the prediction effect of loading rate and crack geometry.

The GRNN model and GPR model was high prediction performance that had and MAPE values equal to and for each model respectively.

References

The original version of this paper is available on https://www.scientific.net/EI.4.15.pdf

Fuan, S., et al., Influence of specimen geometry on mode I fracture toughness of asphalt concrete, Constr Build Mater. 276 (2021) 122181.

Wiangkham, A., A. Ariyarit, and P. Aengchuan, Prediction of the mixed mode I/II fracture toughness of PMMA by an artificial intelligence approach, Theor. Appl. Fract. Mech. 112 (2021) 102910.

Qiao, L., Y. Liu, and J. Zhu, Application of generalized regression neural network optimized by fruit fly optimization algorithm for fracture toughness in a pearlitic steel, Eng. Fract. Mech. 235 (2020) 107105.

Wiangkham, A., A. Ariyarit, and P. Aengchuan, Prediction of the influence of loading rate and sugarcane leaves concentration on fracture toughness of sugarcane leaves and epoxy composite using artificial intelligence, Theor. Appl. Fract. Mech. 117 (2022) 103188.

Lazzarin, P. and R. Zambardi, A finite-volume-energy based approach to predict the static and fatigue behavior of components with sharp V-shaped notches, Int. J. Fract. 112 (2001) 275-298.

Aliha, M., et al., Mixed mode I/II fracture investigation of Perspex based on the averaged strain energy density criterion, Phys. Mesomech. 20 (2017) 149-156.

Chong, E.K. and S.H. Zak, An introduction to optimization. 2004: John Wiley & Sons.

Lewis, C.D., Industrial and business forecasting methods: A practical guide to exponential smoothing and curve fitting. 1982: Butterworth-Heinemann.

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Engineering Innovations, ISSN: 2813-1002, Vol. 4, pp 21-29

doi: 10.4028/p-7540tu

© 2023 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2021-12-14

Revised:
2022-09-01

Accepted:
2022-10-17

Online:
2023-02-15

Optimization of a Developed Multi-Cyclone Using Response Surface Methodology (RSM) to Control Fine Particulate Emission

The original version of this paper is available on https://www.scientific.net/EI.4.21.pdf

N. Hasyimah , M. Rashid and H. Norelyza

¹Department of Civil and Construction Engineering, Faculty of Engineering and Science, Curtin University Malaysia, Miri, Sarawak, Malaysia

AMR Environmental Sdn. Bhd., Pusat Dagangan Seremban 2, Negeri Sembilan, Malaysia

School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru, Johor, Malaysia

rashidyusof.kl@utm.my,

Keywords: Multi-cyclone, collection efficiency, fine particulates, palm oil mill, Response Surface Methodology (RSM).

Abstract

The effects of increasing volumetric air flow rate and inlet particulate loading on overall collection efficiency of MR-deDuster; a developed multi-cyclone system was investigated using various segregated sizes of palm oil mill boiler fly ash. The operating conditions of the fabricated pilot plant scale of the unit were predicted theoretically and screened experimentally. Increasing volumetric air flow rate theoretically will increase the overall collection efficiency, yet the experimental results during screening stage demonstrated contradict finding when the increment of volumetric air flow rate caused the overall collection efficiency to be decreased for a constant particulate loading. Subsequently, the optimization work was done to determine the optimum operating conditions of the system using Response Surface Method (RSM) with Box-Behnken design. The parallel arrangement of multi-cyclone units proved the ability of the system to uniformly disseminate the gas flow with high volume of gas carrier. Nevertheless, excessive pressure drops between each unit of multi-cyclone due to high volumetric air flow rate should be avoided as such condition may lower the overall collection efficiency by allowing dust reentrainment from the hopper to circulate between the cyclones. Through statistical analysis of variance (ANOVA), validation and verification studies, it is suggested that the developed pilot scale multi-cyclone unit would be able to meet the targeted limit of for solid fuel burning equipment industry in Malaysia by operating with optimized volumetric air flow rate of , and maximum inlet particulate loading rate and size of and respectively.

Introduction

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As of 2019, there are a total of 452 palm oil mills which are in operation in Malaysia with 241 palm oil mills are located in Peninsular Malaysia and the rest are in East Malaysia. From the total of Malaysian oil palm planted areas, 2.77 million hectares are planted in Peninsular Malaysia which account to of the total area with the remaining are in East Malaysia for another 3.13 million hectares of oil palm planted area [1]. Come with these large numbers, palm oil mill is one of the industries that need to fulfill Environmental Quality (Clean Air) Regulations 2014 with concentration limit of particulate matter (PM) using solid fuel that can be emitted is regardless the monitoring averaging time and frequency. Within the palm oil mills, boilers and incinerators are the main sources of PM emission. Hence, there are few existing technologies that have been implemented in order to comply with the regulation, including cyclone separators; cyclone and multi-cyclones. Cyclone separators are the most commonly applied in the mills as they are more robust to the high temperature exhaust flue gas, less maintenance required, and most importantly have high efficiency despite the function as a pre-cleaning device. However, in regard of controlling fine particulate emission, the cyclone efficiency is still fall apart compared to other subsequent technologies supplemented with typically below 70% [2].

Nevertheless, a separator with small multi-cyclones has better capability in removing particles than a large diameter cyclone [3]. Realizing the potential of cyclone advancement in capturing fine particulate matter, a prototype system of multi-cyclone namely MR-deDuster has been developed. This system has four miniature identical cyclone which arranged parallelly to optimize its performance by distributing the gas flow uniformly. Performance of a cyclone can be influenced by many factors including the design and dimension of the system itself, and also the operation parameters such as the system's air flow rate, particulate loading rate, and the size range of particulate loaded into the system. Fig. 1 illustrates a schematic diagram of MR-deDuster which showing the inlet, outlet, induced draft (ID) fan, and also emission sampling point. The speed of ID fan literally can be controlled to manipulate the volumetric air flow rate throughout the system.

Fig. 1: Schematic diagram of MR-deDuster multi-cyclone system

Fig. 1. Schematic diagram of MR-deDuster multi-cyclone system

Theoretically, increasing the volumetric air flow rate will increase the performance of a cyclone separator, for instance the overall collection efficiency [4, 5]. As such, this prototype multi-cyclone system has been theoretically predicted using modified [6] semi empirical model introduced by Lapple with volumetric air flow rate ranging from which resulting overall collection efficiency direct proportionally of . This paper presents the experimental approach of the system tested using the same range of volumetric air flow rate. It is found that the experimental result did not satisfy the theoretical estimation which then lead to the optimization on the operating condition of the developed multi-cyclone system using Response Surface Method (RSM) with BoxBehnken design.

Material and Methodology

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Sample. The sample used in this experimental study was palm oil mill boiler fly ash (POMBFA) which had been collected from various palm oil mills and thoroughly mixed to get a homogenous batch. POMBFA was then sieved using Endecotts Laboratory Test Sieve BS 410-1 to segregate the sample size into three categories; smaller than , smaller than , and smaller than 1000 . The same batch of sample was used throughout the study to ensure the accuracy and consistency of the results obtained.

Experimental Screening. The effect on increasing volumetric air flow rate ranging between was randomly examined experimentally using two segregated size POMBFA of smaller than and smaller than with similar inlet particulate loading of . The
sample was fed into the inlet through feeder and the particulate emission was monitored at the sampling point of the stack according to (simplified) USEPA Method 17: Determination of particulate emissions from stationary sources (in-stack filtration method) [7, 8]. The overall collection efficiency of the system was then calculated using Eq. 1 as follow:

where is overall collection efficiency and is concentration of emitted particulate obtained from the stack sampling. The average data were obtained by repeating each run of the experiment and analyzed as based for the optimization study.

Optimization. Based on the results in experimental screening stage, the operating conditions of the system were optimized by using Design Expert® software for Box-Behnken Design of Response Surface Methodology (RSM) [9]. Table 1 indicates the three tested parameters represent by A for volumetric air flow rate, B for inlet particulate loading, and C for segregated particulate size in this optimization study. Seventeen combinations of experimental run were suggested by the software with responses on particulate emitted concentration ( ) and overall collection efficiency (%). The experimental result in this stage was then fit into the second order polynomial equation to represent the effect of changing the operating conditions. Eq. 2 indicates the general form of the model; where Y is the predicted response, is model constant, are linear coefficients, , are cross product coefficients, and , and are quadratic coefficients [10,11]. The statistical model was then evaluated through F-test analysis if variance (ANOVA). Finally, one suggested combination of operating conditions with targeted particulate emission of (equal to - within allowable emission limit) was verified during the validation stage of this optimization study.

Table 1: Tested parameters for optimization study

Operating Condition | Low Level | High Level
A- Volumetric air flow rate [m³/s] | 0.19 | 0.35
B- Inlet particulate loading [g/m³] | 1 | 10
C- Segregated particulate size (smaller than) [μm] | 100 | 1000

Results and Discussion

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Experimental Screening. Fig. 2 presents the overall collection efficiency of the system for two segregated sample sizes based on volumetric air flow rates ranging from . Contradict to the theoretical prediction, the result from experimental study shows that increased volumetric air flow rates would decrease the performance of the multi-cyclone separator. This similar relationship has been reported previously where the researchers found that the fine particulate emission of their cyclone increased with reducing inlet velocity [12] where inlet velocity is directly proportional to pressure drop [13] and so volumetric air flow rate. Besides, the larger sample size of smaller than gives higher performance compared to the smaller segregated size of smaller than . This showing that increasing particulate size will increase the collection efficiency of a cyclone [14]. The collection efficiency can be influenced by many factors including flow interference at the inlet, particulate bounce and re-entrainment from the cyclone wall, effect of particulate loading and effect of surface roughness of cyclone [15].

Pressure drop of the system that is related to the volumetric air flow rate also need to be taken care of even though parallel arrangements of the multi-cyclone system can handle higher gas flows
as excessive pressure drop can cause particulate re-entrainment from the hopper to be circulated back into the system hence reduce the multi-cyclone performance. Cyclone separator with lower pressure at the outlet will discharge more gas from the dust hopper than entering it. The gas which is in ascending vortex then will be higher, thus increased the velocity, but not in radial velocity [14]. This finding was then led to the optimization study of the system.

Fig. 2: Effect of volumetric air flow rate towards overall collection efficiency of MR-deDuster during experimental screening

Fig. 2. Effect of volumetric air flow rate towards overall collection efficiency of MR-deDuster during experimental screening

Optimization. Table 2 presents the result of four out of seventeen total suggested combinations which meet the allowable limit of . Only combinations with lower volumetric air flow rate satisfy this condition. In this optimization experiment, it is found that the collection efficiency increased by decreasing the volumetric air flow rate at constant inlet particulate loading and segregated particulate size as depicted in Table 3.

Table 2: Results on suggested operating conditions using RSM which meet allowable emission limit

Run Order | A [m³/s] | B [g/m³] | C [μm] | Emission Concentration [g/m³] | Overall Collection Efficiency, ηo [%]
1 | 0.19 | 5.5 | 1000 | 0.1455 | 97.35
6 | 0.19 | 1.0 | 500 | 0.1300 | 87.00
13 | 0.27 | 1.0 | 1000 | 0.1146 | 88.54
8 | 0.27 | 1.0 | 100 | 0.0787 | 92.13

Meanwhile, the influence of inlet particulate loading can be observed from Fig. 3. At a constant volumetric air flow rate and segregated particulate size sample of smaller than , increasing inlet particulate loading will increase the overall collection efficiency. This finding is in-line with other reported previous study [4, 16]. However, it is not the case with particulate size smaller than where increasing inlet particulate loading will decrease the overall collection efficiency. Similar finding also has been reported in previous study [17, 18]. It is expected due to the sweeping effect of the coarser particulate, which will carry along the finer particulates to the cyclone wall surface and increase the agglomerates formation which then make it easier to be collected [18]. Besides, based on the results in this stage of study, it is realized that the inlet particulate loading
should be remained constant in order to investigate the effect of volumetric air flow rate of a cyclone separator.

Table 3: Results on suggested operating conditions using RSM for effect of volumetric air flow rate

Run Order | A [m³/s] | B [g/m³] | C [μm] | Emission Concentration [g/m³] | Overall Collection Efficiency, ηo [%]
6 | 0.19 | 1 | 500 | 0.13 | 87
4 | 0.35 | 1 | 500 | 0.3148 | 68.52
9 | 0.19 | 5.5 | 100 | 0.2043 | 96.29
15 | 0.35 | 5.5 | 100 | 2.177 | 60.42
1 | 0.19 | 5.5 | 1000 | 0.1455 | 97.35
7 | 0.35 | 5.5 | 1000 | 0.8089 | 85.29
17 | 0.19 | 10 | 500 | 0.2879 | 97.12
2 | 0.35 | 10 | 500 | 1.6952 | 83.05

Fig. 3: Effect of inlet particulate loading towards overall collection efficiency of MR-deDuster during optimization study using RSM

Fig. 3. Effect of inlet particulate loading towards overall collection efficiency of MR-deDuster during optimization study using RSM

The results from all seventeen runs included five replication experiments were then used to construct an empirical model and evaluated statistically. Table 4 presents the analysis of variance (ANOVA) on parameters for response surface reduced quadratic model of particulate emission concentration, while Eq. 3 shows the reduced quadratic model which has been generated to predict the particulate emission concentration, E from MR-deDuster.

The model's F -value of 18.72 implies that the model is significant with p value is less than 0.0001 which means that there is only probability that this large Model F-value could occur due to noise. This model is also significant for the , and terms since the p -value is less than 0.05 . The 'lack of fit F -value' is not significant reflecting a good condition as it means that the model is fit. The R-squared value of 0.9183 also expressed that the model is fit while the adequate precision that measures the signal to noise ratio with value of 14.85 , which is greater than 4 shows that it is desirable [19, 20] and can be used to navigate the design space.

Table 4: ANOVA for particulate emission concentration response surface reduced quadratic model

Source | Sum of Squares | df | Mean Square | F Value | p-value Prob > F | Remarks | Standard Deviation | R-Squared
Model | 5.30 | 6 | 0.88 | 18.72 | < 0.0001 | Significant | 0.22 | 0.9183
A | 2.08 | 1 | 2.08 | 44.03 | < 0.0001 |  |  |
B | 1.31 | 1 | 1.31 | 27.79 | 0.0004 |  |  |
C | 0.54 | 1 | 0.54 | 11.35 | 0.0071 |  |  |
AB | 0.37 | 1 | 0.37 | 7.92 | 0.0184 |  |  |
AC | 0.39 | 1 | 0.39 | 8.24 | 0.0166 |  |  |
B^2 | 0.45 | 1 | 0.45 | 9.61 | 0.0113 |  |  |
Lack of Fit | 0.40 | 6 | 0.066 | 3.59 | 0.1184 | Not significant |  |

The response surface plots in Fig. 4 and Fig. 5 illustrate the particulate emission concentration response on the interaction between volumetric air flow rate and the inlet particulate loading, and the particulate emission concentration response on the interaction between volumetric air flow rate and segregated particulate size respectively. In order to obtain the lowest sampled particulate emission concentration, the optimum volumetric air flow rate is with inlet particulate loading. However, it is observed during experimental study, that the MR-deDuster system is not recommended to be operated at volumetric air flow rate of since some of particulates were found to be remained on the unit and not completely drawn into the cyclones. Meanwhile, low particulate emission concentration was obtained by lowering the volumetric air flow rate and increase the particulate size samples fraction.

Fig. 4: 3D response surface plot showing the effect of volumetric air flow rate and inlet particulate loading towards particulate emission concentration

Fig. 4. 3D response surface plot showing the effect of volumetric air flow rate and inlet particulate loading towards particulate emission concentration

Fig. 5: 3D response surface plot showing the effect of volumetric air flow rate and particulate size towards particulate emission concentration

Fig. 5. 3D response surface plot showing the effect of volumetric air flow rate and particulate size towards particulate emission concentration

The second statistical analysis evaluated in this optimization stage is on the overall collection efficiency. Table 5 shows the results for response surface linear model of MR-deDuster's overall collection efficiency. It can be seen that the F -value 8.81 of the model is significant with p -value 0.0019 . However, in this case, only the factor for volumetric air flow rate is significant with significant 'lack of fit'. Hence, this model is found to be not fit. Based on this result, only response on the particulate emission concentration was focused for the validation and verification of this optimization study.

Table 6 summarizes the prediction and results for validation and verification study on the particulate emission concentration of the multi-cyclone system. Out of forty-three suggested solutions, only the best combination was chosen to be verified experimentally. Based on the obtained results, the optimum operating conditions for this newly developed multi-cyclone system are volumetric air flow rate of with inlet particulate loading and segregated particulate size of smaller than . Even with difference between software predicted and experimentally verified particulate emission concentration, the response is still within allowable limit with only discrepancy on the overall collection efficiency.

Table 5: ANOVA for overall collection efficiency response surface linear model

Source | Sum of Squares | df | Mean Square | F Value | p-value Prob > F | Remarks
Model | 1005.48 | 3 | 335.16 | 8.81 | 0.0019 | Significant
A | 809.63 | 1 | 809.63 | 21.29 | 0.0005 |
B | 78.31 | 1 | 78.31 | 2.06 | 0.1749 |
C | 117.54 | 1 | 117.54 | 3.09 | 0.1022 |
Lack of Fit | 468.86 | 9 | 52.21 | 8.54 | 0.0269 | Significant

Table 6: Verification on the suggested parameters for validation stage

Source
Sum of
Squares
df
Mean
Square
F Value
p-value
Prob > F
Remarks

Model
1005.48
3
335.16
8.81
0.0019
Significant

A
809.63
1
809.63
21.29
0.0005

B
78.31
1
78.31
2.06
0.1749

C
117.54
1
117.54
3.09
0.1022

Lack of Fit
468.86
9
52.21
8.54
0.0269
Significant

Conclusion

The original version of this paper is available on https://www.scientific.net/EI.4.21.pdf

This study concluded the optimum operating conditions for the pilot plant scale MR-deDuster multi-cyclone to control fine particulate emission with volumetric air flow rate , maximum inlet particulate loading of , and preferable particulate size of smaller than was able to meet the targeted limit for solid fuel burning equipment. Even though higher volumetric air flow rate theoretically will result in higher cyclone performance, it is more favorable to maintain lower volumetric air flow rate as it is directly related to the pressure drop and subsequently the operating cost. Yet, through experimental activity, it was spotted that lower volumetric air flow rate will lead to small amount of particulate to be remained inside the cyclone structure rather than dropped into the hopper. When analyzing the influence of inlet particulate loading towards cyclone performance, it was found that the selection of the loading range was overestimated the capability of the pilot plant scale since the volumetric air flow rate in this plant was lower than in the actual industrial condition by tenfold. Besides, as this developed multicyclone separator aimed to control fine particulate matter, it was observed that with larger size particulate, it can be the driving force to assist in collecting particulate with finer sizes since it has more inertia and easier to hit the inside wall of the cyclone and drop down to the collecting hopper. The performance of this multi-cyclone system should be further verified by scaling down the inlet particulate loading based on the actual plant condition in industry towards this pilot plant scale.

Acknowledgement

The original version of this paper is available on https://www.scientific.net/EI.4.21.pdf

The authors acknowledge the post-graduate research fellowship from Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Kuala Lumpur.

References

The original version of this paper is available on https://www.scientific.net/EI.4.21.pdf

G.K.A. Parveez, E. Hishamuddin, S.K. Loh, M. Ong-Abdullah, K.M. Salleh, M.N.I.Z. Bidin, S. Sundram, Z.A.A. Hasan, and Z. Idris, Oil palm economic performance in Malaysia and R&D progress in 2019, J. of Oil Palm Research. 32 (2020) 159-190.

Y. Hawari, R.M. Halim, and A.A. Aziz, Review on technologies advancement for particulate emission reduction in palm oil mill, Palm Oil Engineering Bulletin. 129 (2018) 42-49.

R.O. Mines, Environmental Engineering Principles and Practice, John Wiley & Sons Ltd., United Kingdom, 2014.

M. Marinuc, and F. Rus, The effect of particle size and inlet velocity on cyclone separation process, Bulletin of the Transilvania University of Brasov. 4 (2011) 117-122.

N. Hasyimah, M. Rashid, and H. Norelyza, Theoretical and experimental performance of MRdeDuster as an axial entry multi-cyclone, Advances in Engineering Research. 29 (2016) 166-171.

H. Norelyza, and M. Rashid, Performance of MR-deDuster: A case study of a palm oil mill plant, Advanced Materials Research. 664 (2013) 133-137.

EPA, Stationary sources control techniques document for fine particulate matter, U. S. Environmental Protection Agency, Research Triangle Park, NY. EPA-452/R-97-001. (1998)

N.H. Hanafi, M.H. Hassim, and M. Rashid, Emission factor establishment for palm oil mill boiler, Jurnal Teknologi. 78 (2016) 101-107.

Reliasoft, Box-Behnken designs for optimizing product performance, Reliability Engineering Resources. 130 (2011).

P.S. Sankar, and R.K. Prasad, Process modeling and particle flow simulation of sand separation in cyclone separator, Particulate Science and Technology: An International Journal. 33 (2015) 385392.

Yunardi, U. Fathanah, E. Munawar, B.P. Putra, A. Razali, and N. Sylvia, Response surface methodology approach to the optimization of cyclone separator geometry for maximum collection efficiency, Int. J. on Adv. Sci. Engineering Information. 9 (2019) 1302-1308.

P.A. Funk, K. Elsayed, K.M. Yeater, G.A. Holt, and D.P. Whitelock, Could cyclone performance improve with reduced inlet velocity?, Powder Technology. 280 (2015) 211-218.

L. Jiang, P. Liu, Y. Zhang, X. Yang, and H. Wang, The effect of inlet velocity on the separation performance of a two-stage hydrocyclone, Minerals. 9 (2019) 1-20.

P.N. Cheremisinoff, Air Pollution Control and Design for Industry, Marcel Dekker, Inc., United States of America, 1993.

K.Y. Kuo, and C.J. Tsai, On the theory of particle cutoff diameter and collection efficiency of cyclones, Aerosol and Air Quality Research. 1 (2001) 47-56.

G.B. Sakura, and A.Y.T. Leung, Experimental study of particle collection efficiency of cylindrical inlet type cyclone separator, Int. J. of Env. Sci. and Development. 6 (2015) 160-164.

K. Tuzla, and J. Chen, Performance of a cyclone under high solids loadings, AIChE Symposium Series. 289 (1992) 130-136.

F.L. Fassani and L. Goldstein Jr., A study of the effect of high inlet solids loading on a cyclone separator pressure drop and collection efficiency, Powder Technology. 107 (2000) 60-65.

D.S. Badkar, K.S. Pandey, and G. Buvanashekaran, Application of the central composite design in optimization of laser transformation hardening parameters of commercially pure titanium using Nd:YAG laser, Int. J. Adv. Manuf. Technol. 59 (2011).

S. Singh, Y.P. Singla, and S. Arora, Statistical, diagnostic, and response surface analysis of nefopam hydrochloride nanospheres using Box-Behnken design, Int. J. of Pharmacy and Pharmaceutical Sci. 7 (2015) 89-101.

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Engineering Innovations, ISSN: 2813-1002, Vol. 4, pp 31-36

doi: 10.4028/p-4wu1vu

© 2023 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2022-07-24

Revised:
2022-09-10

Accepted:
2022-09-30

Online:
2023-02-15

Tensile Strength of 3D Printing Scaffold Design Truncated Hexahedron for Tuberculosis Drug Delivery

The original version of this paper is available on https://www.scientific.net/EI.4.31.pdf

Eka Yuliatin , Dyah Hikmawati *, Aminatun , Aniek Setiya Budiatin , Prihartini Widiyanti , Frazna Parastuti

¹Department of Physics, Faculty of Science and Technology, Airlangga University, Surabaya, Indonesia.

Department of Clinical Pharmacy, Faculty of Pharmacy, Airlangga University, Surabaya, Indonesia.

Biomedical Engineering Study Program, Faculty of Science and Technology, The University of Airlangga, Surabaya, Indonesia

Department of Materials Science and Engineering, College of Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan

eka.yuliatin-2017@fst.unair.ac.id, b* dyah-hikmawati@fst.unair.ac.id, aminatun@fst.unair.ac.id, aniek-s-b@ff.unair.ac.id, prihartini-w@fst.unair.ac.id, frazna.parastuti-2017@fst.unair.ac.id

Keywords: 3D printing scaffold, tensile strength, Injectable Bone Substitute, tuberculosis

Abstract

Mechanical properties are important characteristics of scaffolds as biomaterials implant in tissue engineering. This study focused on the analysis of the tensile strength of the 3D printing scaffold with a geometric design of the truncated hexahedron unit with pore size variation and combined with Injectable Bone Substitute (IBS) paste for treatment of spinal tubercolusis. Five variations of pore size of the scaffold ( , and ) were fabricated from Polylactide acid (PLA) filament using the Fused Deposition Modelling (FDM) method through an ordinary commercial 3D printer. The IBS paste was synthesized from hydroxyapatite (HA), gelatin, hydroxypropyl methylcellulose (HPMC), and streptomycin. The characterization performed in this study were the pore size test with a digital microscope, tensile strength, elongation test, porosity, and contact angle. The 3D printed scaffold formed micropores after injected with IBS paste from a range of . The tensile test results showed that the tensile strength of the 3D printing scaffold increased after being injected with IBS paste. In addition, the elongation test also shows a positive trend with increasing values of elongation after injection of IBS paste. The contact angle test results indicated that the scaffold was hydrophilic. From those characterizations, it could be concluded that 3D printing scaffold meet the criteria of scaffold for bone tissue engineering and drug carrier for tuberculosis.

1. Introduction

The original version of this paper is available on https://www.scientific.net/EI.4.31.pdf

Tuberculosis (TB) is a communicable disease that is a major cause of ill health and one of the leading causes of death worldwide. TB is caused by the bacillus Mycobacterium tuberculosis, which is spread when people who are sick with TB expel bacteria into the air. World Health Organization reported that an estimated 9.9 million people suffered TB in 2020, and Indonesia is one of the largest TB sufferers in the world [1]. Generally, MTB infected the lungs, but it can also infect organs outside the lungs and is called extra-pulmonary tuberculosis. The most common type of extra-pulmonary tuberculosis involves the spine or spinal tuberculosis and is known as Pott's disease.

The bacteria of tuberculosis will cause bone destruction and risk of spinal cord compression [2]. The effective treatment of spinal tuberculosis is a surgical treatment to remove the spinal deformities and replaced them with the scaffold. The development of industrial revolution 4.0 offers an efficient way to make scaffold using a 3D printer. Scaffold is a medium that provides an environment for support stem cells to conduct processes of adhesion, proliferation, and differentiation that will produce the desired new tissue [3]. Scaffold combined with Injectable Bone Substitute (IBS) paste can be used as a drug carrier consisting of Hydroxyapatite (HA), gelatin,

Hydroxy Propyl Methyl Cellulose (HPMC), and Streptomycin, and was proven to be non-toxic and antibacterial [4].

The scaffold was fabricated using the 3D printing Fused Deposition Modeling (FDM) method, based on the principle of layer by layer display to construct a complete 3D material [5]. The 3D geometry model was designed from a Computer-Aided Design (CAD) application, using SolidWorks software. The scaffold requirements were biocompatible, and the physical structure of the scaffold must also qualify for the biological function of the Extracellular Matrix (ECM), which was influenced by the geometric design and pore size of the scaffold [6].

One of the requirements for scaffold as a bone implant is to meet the requirements of human mechanical properties. In tissue engineering scaffolds, porosity is a crucial parameter that affects cell proliferation, attachment, migration and differentiation [7]. The truncated hexahedron design means an intersecting hexagon shape. The intersection creates a new support, and the design appears to be octagonal. Through the addition of struts, it causes an increase in the support load and a reduction in the volume of the scaffold cavity. Based on this description, the strength of the scaffold is influenced by the pore size and geometric design. This study was aimed to synthesize a 3D printing scaffold, then combine it with IBS paste to improve its effectiveness in the treatment of spinal tuberculosis, tensile test was carried out to determine the tensile strength of the scaffold so that it can withstand loads that meet the characteristics of human bones, and contact angle test was also conducted to determine its biocompatibility when interact with human body solution.

2. Materials and Method

The original version of this paper is available on https://www.scientific.net/EI.4.31.pdf

2.1 Materials

The materials used in this study included Poly Lactic Acid (PLA) filaments, the ingredient of IBS paste including hydroxyapatite from bovine bones from PT Inobi, Surabaya, Indonesia, Gelatin (150 bloom Rousselot, Guangdong, China) from cowhide, Hydroxy Propyl Methyl Cellulose (HPMC) (Sigma Aldrich H7509, Singapore), and streptomycin sulfate in 1 gram vial purchased from PT. Meiji Indonesia.

2.2 Manufacturing of 3D printing Scaffold

The manufacturing of scaffold was designed using the Solidworks 2014 application. The pore size was determined by the distance between the fiber (strut). The design was carried out by making a unit cell of a truncated hexahedron with a 2 mm width. The process of the design begins by making octagon shapes on the front, top, and right. Then flipping planes at an angle of , so the design framework is obtained. All of the sides are swept using Swept Boss/Base, so a truncated hexahedron unit cell is obtained (Fig. 1).

The process of merging or assembly is carried out on the unit cell to obtain a geometric design. The tensile test was designed to follow ASTM D638 (Fig. 2) using a ratio of 1:0.5 [8]. The extension of the Solidworks 2014 application in the form of .stl form, via the Cura from Ultimaker application by looking at the slicing parameters of the design. The designed scaffold will be printed using a 3D printing machine with the FDM method with the nozzle used having a hole diameter of 0.4 mm . The filament used is Polylactic Acid (PLA) with a diameter of 1.75 mm . Strut size and distance between struts of designed scaffold can be seen in Table 1.

Table 1. Strut size and distance between struts of designed scaffold

Pore size of Scaffold, [μm] | Distance between strut, [mm] | Width of scaffold, [mm] | Radius of strut,[mm]
600 | 0.6 | 2 | 0.7
800 | 0.8 | 2 | 0.6
1000 | 1 | 2 | 0.5
1200 | 1.2 | 2 | 0.4
1400 | 1.4 | 2 | 0.3

Figure 1. Unit design of bone scaffold

Figure 1. Figure 1. Unit design of bone scaffold

Figure 2. Design of tensile test sample

Figure 2. Figure 2. Design of tensile test sample

2.3 IBS Paste Synthesis

IBS paste synthesis was conducted according to previous research [4]. At first, gelatin was dissolved in distilled water at for 1 hour. At the same time, HPMC was dissolved in distilled water at for 1 hour, then the solution was cooled until the temperature became . In the dissolved gelatin solution, hydroxyapatite was added with a composition ratio of HA: gelatin 65:35 w/w and stirred for 1 hour. After the solution became homogeneous, the next step was added streptomycin as much as of the total mass of HA, gelatin, and HPMC, and dissolved it again until the solution became homogeneous. In the last step, the HPMC solution was added to the HA-gelatin-streptomycin at a mixing temperature of for 6 hours.

2.4 Sample Characterization

Scaffold pore size test was conducted using the digital microscope to determine the pore size formed on the scaffold both before and after the injection of IBS paste. The porosity test was conducted to determine the percentage of pores formed in the scaffold. The density of PLA is [9]. The mechanical strength of the scaffold was obtained by performing tensile tests both on the scaffold without and after injection of IBS paste. The tensile strength and elongation can be calculated from stress-strain graph. The porosity value of the scaffolds was measured by liquid displacement. In this method ethanol was used as the displacement liquid because it penetrated easily into the pores. The contact angle test was conducted to determine the hydrophilicity and biocompatibility of the scaffold.

3. Result and Discussion

The original version of this paper is available on https://www.scientific.net/EI.4.31.pdf

3.1 3D Printing Scaffold Injected with IBS Paste

The results of the 3D Printing scaffold macroscopically showed that it has good interconnectivity between pores. While the pore size design is and , after printed, measured and produced and on average respectively. Then scaffold was injected with IBS paste for drug carrier application in the treatment of spinal tuberculosis. In the scaffold that has been injected with IBS paste, the average micropores are . The porosity test obtained the data of porosity respectively , and .

Fig. 3. Samples of 3D printing scaffold for tensile test (a) before and (b) after IBS Paste Injected A) 600 , B) , C) , D) , E)

Fig. 3. Samples of 3D printing scaffold for tensile test (a) before and (b) after IBS Paste Injected A) 600 , B) , C) , D) , E)

3.2 Tensile Test

The tensile test was conducted to determine the maximum stress that could be received from the scaffold before fractured with applied axial force. The tensile test was conducted 2 times for each variation of the scaffold without IBS and after IBS paste injection. The tensile strength test resulting a graph of stress and strain as shown in Fig. 4 for the PLA scaffold, before and after injected with IBS paste. And Fig. 5 and 6 shown both the tensile strength and elongation.

Fig. 4. The tensile strength (a) before and (b) after IBS Paste Injected

Fig. 4. The tensile strength (a) before and (b) after IBS Paste Injected

Fig. 5. The tensile strength test result

Fig. 5. The tensile strength test result

Fig. 6. The elongation test result

Fig. 6. The elongation test result

The tensile strength and elongation of the scaffold decreased with increasing pore size. The tensile strength value was higher for scaffolds injected with IBS paste, from to and elongation from to . Based on these results, the trend of the relationship between pore size and tensile strength and elongation is obtained. Based on the literature [10], the ultimate tensile strength of vertebral trabecular bone is . So that based on this results, the tensile strength of the scaffold qualifies for the use of tissue engineering. The mechanical properties determine the load of scaffold that can withstand to support
the human body. IBS paste can increase the tensile value because IBS paste fills the volume of the cavity in the scaffold so that the tensile support area becomes larger.

Based on research conducted by [11], a study of the effect of pore size on the mechanical properties of the box design scaffold with a pore size range was carried out using PLA filament. The result was obtained that a negative correlation of pore size and tensile strength. Scaffold to qualified bone repair requirements, smaller pore sizes could be beneficial for the mechanical strength of the porous scaffold. This is followed by the results obtained in this study, which a negative trend relationship between pore size and tensile strength.

3.3 Contact Angle Test

The contact angle test was conducted using the digital microscope and supported by using the HiView application to observe the angle formed between the liquid and the scaffold. Based on the results of the contact angle, showed that the scaffold which before and after IBS paste injected was hydrophilic with a contact angle of less than . There are variations of the pore size of the scaffold that obtained the effect of pore size on the mechanical properties of the scaffold. The porosity of the scaffold is required for supporting the growth of new bone cells. The scaffold porosity of was qualified to support cell proliferation [12]. Based on the data of this study, the porosity of means that the scaffold suitable for its use in supporting the regeneration of bone cells.

Fig. 7. The contact angle test results (a) before IBS Paste Injected, (b) after IBS paste injected

Fig. 7. The contact angle test results (a) before IBS Paste Injected, (b) after IBS paste injected

The contact angle result was obtained that scaffold has hydrophilic behavior with angle of for scaffolds without IBS paste and for scaffold injected by IBS paste. The scaffold was hydrophilic and qualified to be applied in tissue engineering. The hydrophilic behavior shows that the scaffold has good biocompatibility properties when being applicated in the body.

4. Conclusion

The original version of this paper is available on https://www.scientific.net/EI.4.31.pdf

3D printing scaffolds with various pore sizes were synthesized with porosity of , that could support the cell proliferation process. Hydroxyapatite combined with gelatin, HPMC, and streptomycin, then injected into the 3D printing scaffold as a means of drug carrier and a method of treating spinal tuberculosis. The tensile strength value was higher for scaffold which injected with IBS paste, from to and elongation from to , porosity values and contact angle for scaffold without IBS paste, and for scaffold injected with IBS paste. This result indicated that the scaffold was hydrophilic. From those characterizations, it could be concluded that 3D printing scaffold qualified the criteria of scaffold for bone tissue engineering and suitable method for drug delivery in spinal tuberculosis treatment.

Acknowledgments

The original version of this paper is available on https://www.scientific.net/EI.4.31.pdf

The authors would like to thank Universitas Airlangga for the funding of this research [Decree number 8/E1/KPT/2021 and Contract agreement number 4/E1/KP.PTNBH/2021 and 677/UN3.15/PT/2021].

References

The original version of this paper is available on https://www.scientific.net/EI.4.31.pdf

World Health Organization, 2021. Global Tuberculosis Report 2021 (Geneva: WHO). https://www.who.int/publications/i/item/9789240037021

Liang, X., Weiyang, Z., Zhengxue, Q., Xiao-Ji, L., and Dian-Ming, J. 2019. One-Stage Posterior Debridement with Transverse Process Strut as Bone Graft in The Surgical Treatment of Single-Segment Thoracic Tuberculosis. Medicine. 98 (47). . 0000000000018022

Li, Y., Yu, B., Ai, F., Wu, C., Zhou, K., Cao, C., Li, W., 2021. Characterization and evaluation of polycaprolactone/hydroxyapatite composite scaffolds with extra surface morphology by cryogenic printing for bone tissue engineering. Materials & Design (205):109712.

Maulida, H., Hikmawati, D., and Budiatin, A. S. 2019. Injectable Bone Substitute Paste Based on Hydroxyapatite, Gelatin and Streptomycin for Spinal Tuberkulosis. Journal of SCRTE. 3 (2).

Kim CG, Han KS, Lee S, Kim MC, Kim SY, Nah J. Fabrication of biocompatible polycaprolactone-hydroxyapatite composite filaments for the FDM 3D printing of bone scaffolds. Appl Sci 2021;11:6351.

Rana D., Ratheesh G., Ramakhrishna S., Ramalingam M., 2017, Nanofiber Composites in Cartilage Tissue Engineering, Nano Composites for Biomedical Applictions, 3325-344.

Patel, D.K. Lim, K.T. 2019. Biomimetic polymer-based engineered scaffolds for improved stem cell function, Materials (Basel) 12 (18).

ASTM International. Standard Test Method for Tensile Properties of Plastics. accessed from accessed on 24nd July 2022 at 10.09 AM

Bitfab, The densities of all 3D printing materials, accessed from accessed on 22nd July 2022 at 10.33 AM.

Ohman-Magi, C., Holub. O., Wu, D., Hall, R.M., Persson, C., 2021. Density and mechanical properties of vertebral trabecular bone-A review. JOR Spine. 4:1176.

Zhao, H., Li, L., Ding, S., Liu, C., and Ai, J. 2018. Effect Of Porous Structure and Pore Size on Mechanical Strength of 3D-Printed Comby Scaffolds. Materials Letters. 223: 21-24.

Chen, Y., Frith, J., Manshadi, A., Attar, H., Kent, D., Soro, N. D., Bermingham, M., and Dargusch, M. 2017. Mechanical properties and biocompatibility of porous titanium scaffolds for bone tissue engineering. Journal of the Mechanical Behavior of Biomedical Materials. 75: 169174.

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The original version of this eBook is available on https://www.scientific.net/book/engineering-innovations-vol-4/978-3-0364-1008-1

Engineering Innovations, ISSN: 2813-1002, Vol. 4, pp 37-46

doi: 10.4028/p-8a27xd

© 2023 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2022-09-30

Revised:
2022-10-11

Accepted:
2022-10-13

Online:
2023-02-15

A Novel Epitope Dataset: Performance of the MCL-Based Algorithms to Generate Dataset for Graph Learning Model

The original version of this paper is available on https://www.scientific.net/EI.4.37.pdf

Binti Solihah , Aina Musdholifah , Azhari Azhari

¹Department of Informatic, FTI, Universitas Trisakti, Indonesia

Department of Computer Science and Electronics, FMIPA, Universitas Gadjah Mada, Indonesia

binti@trisakti.ac.id, aina_m@ugm.ac.id, arisn@ugm.ac.id

Keywords: Epitope Dataset, PS-MCL, graph clustering, graph learning model.

Abstract

Naturally, the epitope dataset can be presented as a graph. Dataset preparation in the previous methods is part of model development. There are many graph-based classification and regression methods. Still, it is difficult to identify their performance on the conformational epitope prediction model because datasets in a suitable format are unavailable. This research aims to build a dataset in a suitable format to evaluate kernel graph and graph convolution network. This dataset, which results from graph clustering on graph antigens, can be used to identify the performance of many graph neural network-based algorithms for conformational epitope prediction. The complexes that meet the criteria for forming a conformational epitope prediction dataset from previous studies were downloaded from the Protein Data Bank. Raw datasets in the form of specific exposed antigen chain residues are labeled as epitope or non-epitope based on their proximity to the paratope. The engineering features in the raw dataset are derived from the structure of the antigenantibody complex and the propensity score. Aggregating atomic-level interactions into residual levels create an initial graph of the antigen chain. The MCL, MLR-MCL, and PS-MCL are graph clustering algorithms to obtain labeled sub-clusters from the initial graph. A balance factor parameter is set to several values to identify the optimal dataset formation based on minimal fragmentation. The output of the MCL algorithm is used as a baseline. As a result of the fragmentation analysis that occurs, the MLR-MCL algorithm gives the best model performance at a balance factor equal to 2 . PS-MCL gives the best performance at a value of 0.9 . Based on the minimum fragmentation, the MLR-MCL algorithm provides the best model performance compared to MCL and PS-MCL. The dataset in a format according to benchmarking dataset can be used to identify the characteristics of antigen subgraphs formed from the graph clustering process and to explore the performance of graph-based learning conformational epitope prediction models such as graph convolution networks.

Introduction

The original version of this paper is available on https://www.scientific.net/EI.4.37.pdf

The conformational epitope is the antigen part bound by the antibody in the recognition phase of the immune system [1] Identification of this part of the antigen required in developing drugs, vaccines, and therapeutic vaccines [2,3] using a protein engineering approach [4]. The conformational epitope consists of amino acid residues that have spatial proximity in the binding state and consist of segments in the primary sequence. Epitopes in an antigen chain can be single, multiple, or overlapping. The residues which conform epitope may form a network that has not identified what is behind the conformation. However, the statistical analysis showed that specific pairs of amino acids differed significantly from other pairs on the epitope [5,6].

Graphs can abstract complex systems with interactions between objects, such as molecular graphs, social networks, and biological networks. Graph generation and transformation using machine learning can potentially solve complex problems such as protein folding, statistical modeling, and molecule design [11]. The use of the graph approach in the prediction of conformational epitopes is still minimal. Zhao et al. initiated this approach by implementing MCL to obtain subgraphs of antigens based on edge weights and topology graphs [12]. Another study by the same researcher used the coupling graph to improve the model's performance by utilizing the frequent coupling subgraph pattern [13]. The other model applies graph partitioning to obtain a subgraph of the residual graph
surface and continues with seed expansion to obtain a candidate epitope [14]. Wang et al. introduced conformations in graph formation [15]. Zhao applied a graph kernel for classification, but Wang used the Graph Convolution network.

The availability of epitope prediction datasets in graph representation is needed to explore many graphs computational approaches to build epitope prediction models. Graph dataset has elements in the form of nodes, edges, node attributes, and edge attributes. SNAP dataset [16], MoleculNet [17], TUDataset [18], GraphGT [19], Open Graph Benchmark (OGB) [20] is a graph dataset originating from many different domains. SNAP contains 80 different datasets and is a system for generating, manipulating, and analyzing graphs and networks. MoleculeNet is the molecular dataset built on DeepChem for classification and regression. There are 17 datasets from the domains of quantum mechanics, physics chemistry, biophysics, and physiology on MoleculeNet. OGB's molecular graph is an improvement from the MoleculNet version. It has a data loader feature and additional features on Molecular Graph. The addition of features to the nodes and edges of the Molecular Graph in OGB can improve the performance of model [20]. The TUDataset contains 120 datasets and baseline methods with the Python interface. GraphGT contains 36 datasets from 9 domains, including protein, brain network, vision, transportation science, social network, and molecule. GraphGT provides a Python API for accessing data and is available for download via https://graphgt.github.io/.

Graph learning research such as Deep Walk [23] and Graph Convolution Network [24] has received attention from many researchers and is growing fast. Many graph learning approaches are applied in node level classification, link edge, and graph classification. This approach is naturally appropriate for many real-world problems. The graph-based computational approach opens the opportunity to produce a better conformational epitope prediction model than the previous methods. The application of the graph method for the prediction of the conformational epitope is still minimal compared to the methods that have been developed. One of the causes is the availability of datasets in graph representations that follow benchmarking standards in graph learning. This study aims to create a dataset in a graph representation whose nodes and edges are enriched with features to support the prediction of conformational epitopes. Residues with proximity at the atomic level are aggregated to form a graph with nodes enriched with features calculated at the residual level. Statistically, the features at the epitope residues are significantly different from the non-epitope residues. The antigen graph is then clustered using the MCL graph clustering algorithm and its derivatives to obtain the non-epitope and epitope subclusters. Very few sub-clusters were detected containing epitope residues, so for optimization of sub-cluster formation, a threshold value was used in the form of the percentage of epitope residues in each category. The resulting dataset format follows the TUDataset format, whose explanation is in the URL File Format | TUDataset (chrsmrrs.github.io)

The following section will discuss the data preparation and methods for generating epitope predictive datasets in graph representation. Next will be presented the results of the experiment, an analysis of the results, and conclusions.

Dataset Collection

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The dataset was collected and preprocessed using the procedure described in Solihah et al. [25]. Raw data in the form of 3d chain antigen structures containing information on residues exposed at the atomic level and labeled epitope and non-epitope was taken from Gao et al. [7]. These residues are enriched with features of structural analysis with PSAIA, which include ASA, RSA, and Protrusion Index [26], Contact Number [27], QSE [28], HSE [29], AAIndex [30], B Factor [31] and Log Odd Ratio [32]. The dimension of the vector properties is is used to reduce its dimensions to a vector of length 25.

Experimental Procedure

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The experimental procedure is described in the block diagram in Fig 1. There are three steps to construct a dataset: (1) Initial graph generation; (2) Sub-cluster formation from the initial graph; (3) Dataset generation.

Initial graph generation. This stage aims to obtain an initial undirected graph representation of residues exposed to an antigen chain. An edge is formed if the distance between the residue and other residues is within the allowable distance. Edge weights were calculated based on the log odds ratio of the frequency pairs on the epitope compared to the frequency of the pairs on the non-epitope.
Sub-cluster formation. The formation of the dataset begins with applying MCL-based clustering to each input in the form of a graph from the data preparation stage, as describe in Fig 2 . The MCL algorithm used is MCL (21), MLR-MCL [22], and PS-MCL [11]. At this stage, an MCL-based algorithm is identified that produces the best cluster. Analysis of the number of clusters formed and the purity of the clusters became the reference parameters in determining the quality of the clusters. The balancing factor parameter ( r ) and coarse mode (shotgun coarsening and heavy edge matching) are the parameters that are optimized to get the best cluster.
Dataset generation. Save the labeled cluster data into a file format following the file format on the TUDataset. Storage procedure is defined to create the graph dataset with a file format such as TUDataset.

Fig. 1. The step by step of experimental procedures

Fig. 1. The step by step of experimental procedures

MCL-based Sub Graph Clustering

The original version of this paper is available on https://www.scientific.net/EI.4.37.pdf

The MCL algorithm works on data in graph representation. A graph is expressed as a set of nodes and edges, where edges connect the nodes in a graph. MCL is a flow-based graph clustering method that applies the random walk concept. Visits within the caster have a higher probability than visits to other clusters. The existence of edges in flow-based graph clustering states the probability of a flow occurring from node to node. If a node is connected to 3 other nodes, then the transition probability of that node to another node is . In a weighted graph, edges have weights so that the transition probability matrix Mij is a column-normalized matrix of the adjacency matrix plus selfloop (Eq. 1).

where n is the number of nodes in the graph. The MCL algorithm is built by three basic operations: expand, inflate, and prune. Expansion is done by raising the transition probability matrix. The expansion operation allows flows to be connected to different clusters in the graph. The inflate operation powers the transition probability matrix M by a real number r followed by column normalization. Inflation is used to strengthen and weakening flow (Eq. 2).

MCL is one of the superior graph clustering algorithms in the bio-networking field because the clustering results are in the form of small sub-graphs ( 1 to 3 nodes). The MCL algorithm has attracted researchers' interest in developing its derivatives, such as Regularized MCL, MLR-MCL, and PSMCL. If viewed from the number of constituent residues, the conformational epitopes of B cells are categorized as clusters with sizes between 5 and 30 constituent residues. In terms of the number of constituent residues, the epitope is suitable if identified by utilizing the derivative of the MCL algorithm compared to the original MCL, which was identified as causing fragmentation.

Fig. 2. MCL-based sub graph clustering

Fig. 2. MCL-based sub graph clustering

The dataset formation stage is presented in Fig. 3. Input is in the form of 3d structure data from the exposed residues of the antigen chain, each with the "epitope" or "non-epitope" label. The output is a collection of sub-graphs with the "epitope," "mixed," or "non-epitope" label, which is stored in separate files. Details of the dataset formation process are as follows:

Build a graph from the input data following the graph formation procedure in Gao et al.[7]

Calculate the edge weights following the procedure in Gao [7]

Calculate the ASA, RSA, PSAIA, and AAIndex features of the residues in the graph and store them as attributes of each node of the graph

Perform graph clustering using PS-MCL algorithm with parameters that optimize cluster results to obtain three subgraph categories. The first category is non-epitope clusters
containing < 30% epitope residues. The second categories consist of mixed clusters containing epitope residues and . The third category is epitope clusters containing epitope.

Write down all the subgraphs resulting from the process of step No. 4 into the four files.

Fig. 3. Epitope dataset construction

Fig. 3. Epitope dataset construction

Statistical Analytic Procedures

The original version of this paper is available on https://www.scientific.net/EI.4.37.pdf

Among the critical issues in the application of the MCL algorithm is the formation of small clusters or fragmentation [11]. At the initial stage, min and max statistical analyses are conducted to answer the following research questions related to the problem of fragmentation:
Q1. How does the selection of the balance factor affect the distribution of clusters and nodes of the formed cluster?
Q2. does the balance factor on the MLR-MCL and PS-MCL algorithms have the same effect on the distribution of clusters and nodes in the cluster?
Q3. Which factor balance gives clustering results with the minimum number of small clusters and the minimum number of nodes?

Experiment Result

The original version of this paper is available on https://www.scientific.net/EI.4.37.pdf

We applied the MCL, MLR-MCL, and PS-MCL algorithms to the initial graph antigen data at the residual level and identified the distribution of the number of clusters and the number of nodes formed in each cluster. The node distribution in each cluster generated by the MCL algorithm is used as a baseline to measure the fragmentation in the cluster results of the MLR-MCL and PS-MCL algorithms. Table 1 presents the results of graph clustering with the MCL algorithm. The number of small-sized clusters formed is 120 clusters composed of 325 nodes. The number of clusters with more than three nodes is 3641 clusters and is composed of 32667 nodes. The percentage of the small clusters, as much as 3.19 percent, is express fragmentation. The percentage of nodes that form fragmentation compared to total nodes is 0.99 percent.

Table 1. The distribution clusters and nodes by MCL Algorithm

number of clusters
<= 3
number of clusters
> 3
number of nodes in
the cluster <=3
number of nodes in the
cluster > 3

120
3641
325
32667

In the next experiment, the effect of the balance factor on fragmentation was identified. As presented in Table 2 and Table 3, several balance factor values were tested for the MLR-MCL and PS-MCL algorithms to determine the best balance factor that produces minimal fragmentation. The balance
factor values of 1, 1.5, and 2 were selected following the experiment of Lim et al.[]. Another balance factor value was tried to identify the optimal fragmentation probability at a balance factor of less than 1.

Based on the experimental results presented in Table 2, in the PS-MCL algorithm, the number of small clusters with a balance factor of less than one is less than the number of small clusters with a balance factor of more than 1 . From all experiments conducted, the minimum number of clusters is Small size is obtained at a balance factor of 0.9 , where the number of small clusters formed is 192 clusters with the number of nodes that compose as many as 521 . The percentage of small casters compared to all clusters formed is 4.13 percent, while the percentage of constituent nodes compared to the total number of nodes is 1.58 percent. Compared with the percentage of fragmentation in the MCL algorithm, the percentage of fragmentation in PS-MCL is greater than that in MCL. At balance factor , fragmentation occurs with a percentage of 16.5 percent and is the largest percentage of fragmentation in PS-MCL.

Table 2. The distribution of number of clusters and nodes in various balance factors of PS_MCL

balance factor | number of clusters <= 3 | number of clusters > 3 | number of nodes in the cluster <=3 | number of nodes in the cluster > 3
0 | 246 | 3076* | 666 | 32326
0.1 | 296 | 3392 | 781 | 32211
0.2 | 590 | 3598 | 844 | 32148
0.3 | 392 | 3783 | 912 | 32080
0.4 | 405 | 3945 | 944 | 32048
0.5 | 381 | 4063 | 952 | 32040
0.6 | 331 | 4191 | 840 | 32152
0.7 | 271 | 4305 | 716 | 32276
0.8 | 238 | 4389 | 644 | 32348
0.9 | 192* | 4456** | 521* | 32471**
1 | 730** | 3682 | 5344** | 27648*
1.5 | 690 | 3722 | 1596 | 31396
2 | 690 | 3722 | 1596 | 31396

note: *: minimum; **: maximum

The experimental results of several balance factor values in the MR-MCL are presented in Table 3. The number of small clusters produced in various balance factors of the MLR-MCL algorithm is generally smaller than in the PS-MCL algorithm. If in the PS-MCL, the optimal fragmentation is at the balance factor , then in the MLR-MCL, the optimal fragmentation is obtained at the balance factor . The fragmentation that occurs in the balance factor between 0 to 1 is negatively correlated with the balance factor. In MLR-MCL, the distribution of clusters and their constituent nodes at balance factor is the same as at balance factor . The minimum number of small-sized clusters formed occurs at balance factor clusters ( 0.38 percent) with the number of nodes that make up 39 ( 0.11 percent).

Table 3. The distribution of number of clusters and nodes in various balance factors of MLR-MCL

balance factor | number of clusters <= 3 | number of clusters > 3 | number of nodes in the cluster <=3 | number of nodes in the cluster > 3
0 | 49 | 3982 | 136 | 32856
0.1 | 173 | 3301* | 472* | 32520*
0.2 | 175 | 3469 | 457 | 32535
0.3 | 176* | 3594 | 429 | 32563
0.4 | 147 | 3702 | 361 | 32631
0.5 | 130 | 3765 | 333 | 32659
0.6 | 109 | 3821 | 288 | 32704
0.7 | 88 | 3878 | 239 | 32756
0.8 | 74 | 3916 | 203 | 32789
0.9 | 60 | 3950 | 163 | 32829
1 | 49 | 3982 | 136 | 32856
1.5 | 26 | 4124 | 69 | 32923
2 | 16* | 4177** | 39* | 32956**

*:min, **: max

Discussion

The original version of this paper is available on https://www.scientific.net/EI.4.37.pdf

The model's best performance in forming graph datasets for predicting the conformational epitope is inversely proportional to the fragmentation that occurs. Fragmentation is represented by the number of small clusters ( 1 to 3 nodes) and the nodes involved in their formation. The main factor that influences the occurrence of fragmentation is the balance factor. The best model performance from the MLR-MCL and PS-MCL models is presented in Table 4. The best model performance is obtained from the MLR-MCL model with a balance factor of 2, while the PS-MCL model occurs at a balance factor of 0.9 . In this case, MLR-MCL is better than PS-MCL. The number of small clusters in PSMCL is much more than in MLR-MCL by 12 times, with the number of nodes contributing more than 13 times. This condition can be observed on the gray and blue bars in Fig 4.

Table 4. The best performance of PS-MCL and MLR-MCL

Algorithm | balance factor | number of clusters <= 3 | number of clusters > 3 | number of nodes in the cluster <=3 | number of nodes in the cluster > 3
PS-MCL | 0.9 | 192 | 4456 | 521 | 32471
MLR-MCL | 2 | 16 | 4177 | 39 | 32956
MCL |  | 120 | 3641 | 325 | 32667

Fig. 4. Performance comparison on fragmentation

Fig. 4. Performance comparison on fragmentation

MCL implementation. PS-MCL and MLR-MCL in other datasets were performed by Lim et al.[11]. According to Lim et al., the number of nodes that build small clusters in PS-MCL is less than in MLR-MCL. The experimental results on the antigen dataset showed different results, where the number of nodes that formed fragmentation in MLR-MCL was less than in PS-MCL.

This study still does not yet identify the distribution of epitope and non-epitope residues in the formed sub-clusters. Previous research shows that if the sub-cluster contains epitope nodes greater than nonepitope, then it is categorized as an epitope sub-cluster. This proportion has not been proven to capture critical features of an epitope or non-epitope cluster. Experiments are needed to show the classification model's performance from the dataset formed based on various percentages of the epitope in the sub-cluster labeled with the epitope. In addition, the distribution of epitope residues in small clusters is not yet known whether they dominate or not.

The experimental results of the fragmentation measurement of the MCL, PS-MCL, and MLR-MCL algorithms on the antigen dataset show that the MLR-MCL approach is better than the MCL and PSMCL approach. Experimental results with PS-MCL showed greater fragmentation than MCL. When viewed from the number of clusters measuring more than three nodes, both PS-MCL and MLR-MCL resulted in a higher number of clusters. It can be roughly stated that the cluster size in MCL is larger than in PS-MCL and MLR-MCL.

Conclusion

The original version of this paper is available on https://www.scientific.net/EI.4.37.pdf

There are many graph-based classification and regression methods. Still, it is difficult to identify their performance on the conformational epitope prediction model because datasets in a suitable format are unavailable. This research aims to build a dataset in a suitable format to evaluate kernel graph and graph convolution network. The MCL, MLR-MCL, and PS-MCL are graph clustering algorithms to obtain labeled sub-clusters from the initial graph. A balance factor parameter is set to several values to identify the optimal dataset formation based on minimal fragmentation. The output of the MCL algorithm is used as a baseline. As a result of the fragmentation analysis that occurs, the MLR-MCL algorithm gives the best model performance at a balance factor equal to 2 . At the balance factor 0.1, MLR-MCL generates 176 small clusters which use 472 nodes to form a small cluster. PS-MCL gives
the best performance at a value of 0.9 . At balance factor 1, PS-MCL produces 730 small clusters with5344 nodes. Based on the minimum fragmentation, the MLR-MCL algorithm provides the best model performance compared to MCL and PS-MCL. Each subgraph must be labeled epitope and non-epitope to be used for classification. It is necessary to identify a threshold of the percentage of residue epitope in the subgraph to label the subgraph as epitope or non-epitope. Previous researchers used a limit on the number of epitopes more or equal to .

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The original version of this paper is available on https://www.scientific.net/EI.4.37.pdf

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The original version of this eBook is available on https://www.scientific.net/book/engineering-innovations-vol-4/978-3-0364-1008-1

Engineering Innovations, ISSN: 2813-1002, Vol. 4, pp 47-66

doi: 10.4028/p-h0cef4

© 2023 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2022-08-12

Revised:
2022-10-19

Accepted:
2022-10-23

Online:
2023-02-15

A Novel Ensemble of Support Vector Machines for Improving Medical Data Classification

The original version of this paper is available on https://www.scientific.net/EI.4.47.pdf

Phuoc-Hai Huynh and Van Hoa Nguyen

¹Faculty of Information Technology, An Giang University, An Giang, Vietnam

Vietnam National University Ho Chi Minh city, Vietnam

hphai@agu.edu.vn, nvhoa@agu.edu.vn

Keywords: Medical Data Classification, Ensemble Learning, Support Vector Machines

Abstract

In recent years, the increasing volume and availability of healthcare and biomedical data are opening up new opportunities for computational methods to enhance healthcare in many hospitals. Medical data classification is regarded as the challenging task to develop intelligent medical decision support systems in hospitals. In this paper, the ensemble approaches based on support vector machines are proposed for classifying medical data. This research's key contribution is that the ensemble multiple support vector machines use the function kernel in the style of gradient boosting and bagging to produce a more accurate fusion model than the mono-modality models. Extensive experiments have been conducted on forty benchmark medical datasets from the University of California at Irvine machine learning repository. The classification results show that there is a statistically significant difference ( p -values ) between the proposed approaches and the best classification models. In addition, the empirical analysis of forty medical datasets indicated that our models can predict diseases with an accuracy rate of 82.82 and 81.76 percent without feature selection in the preprocessing data stage.

Introduction

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In recent years, the use of machine learning in medical diagnostics to assist physicians in disease diagnosis has become widespread. In practice, a doctor often diagnoses a disease based on a patient's symptoms and signs. For this reason, the physician's experience highly affects the diagnostic accuracy. The clinical decision support systems (CDSS) has emerged as a realistic strategy to help doctors diagnose patients swiftly and correctly in an effort to improve healthcare quality [1]. Machine learning is used CDSS to predict and analyze diagnostic decisions to improve inpatient care more accurate and faster diagnoses as well as support doctors' decision-making [2]. In the field of medical informatics, the increase in the accuracy of classification algorithms is regarded as one of the most challenging tasks whose aim is to enhance the diagnosis, prediction, and treatment of disease [2]. Several classifying algorithms have been used in the analysis of medical diagnosis without feature selection. They include support vector machines (SVMs) used by [3], [4] random forests (RF) in [5], [6], nearest neighbors ( ) used by [7], [8], decision trees (DT) exploited in [7], [9], naive Bayes (NB) in [10], and logistics regression (LR) in [11]. Even though many studies have been done and reported in the literature, there is still room for improvement. In medical diagnosis, more and more studies have proposed novel classification algorithms to improve the accuracy of these algorithms. However, the No-Free-Lunch theorem demonstrated no perfect classifier [12]. It has motivated studies to build new classification models for medical data [13].

Recently, ensemble learning is an algorithm which uses multiple learners to boost a monomodality model's prediction performance in order to maximize classification accuracy with a greater capacity for generalization. In fact, weak learners are used to build ensemble models in the majority of the study analyses of ensemble methods mentioned above. Therefore, it is essential to examine ensembles based on non-weak classifiers, such as SVM. Although several works presented insight for SVM ensemble theory and applications [14], [15]. However, SVM ensembles have not been investigated thoroughly like the decision tree ensemble [16]. In the medical field, SVM ensembles haven't been tested against a lot of data sets for disease diagnosis. This paper examines SVM
ensembles using two approaches to build models with various medical datasets in order to close this gap. This work explores the utility of ensemble SVMs for classifying medical data through two contributions.

Firstly, two ensemble frameworks (Bagging and Gradient boosting) are proposed for medical data classification to combine SVM models using the radial basis function (RBF) kernel to produce an ensemble of classifiers that is more accurate than a single SVM model. The key concept is to use RBF kernel-based constructs for SVM ensembles to strengthen SVM.

Secondly, empirical analysis for forty medical datasets is designed to select suitable classification models. We have been performed a comparative testing of several classifiers namely Linear SVM (LSVM) and SVM using RBF kernel, KNN, DT, NB, LR and ensemble of decision trees include random forest (RFs), bagging (BA-DTs) and gradient boosting (GB-DTs). The accuracy (ACC) as well as Area Under the ROC Curve (AUC) of the 12 algorithms are used to compare in experiments.

The remaining sections of the paper are structured as follows. In the section titled "Related works," the classification of medical data is briefly discussed. In the "Materials and Methods" Section, we present our models. We analyze the experiment and present the numerical test results in Section Evaluation before completing in Section Conclusion.

Related Works

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After analyzing the literature, we discovered numerous approaches to classifying diseases. These applications involve individual learning techniques as well as combining techniques to improve the performance of models. In the session, we will discuss these techniques and their applications.

On the one hand, there are many individual classifying techniques to predict disease. As shown by [17], [18], the KNN algorithm is used to identify heart disease. According to [10], NB and KNN are used in the diagnosis of breast cancer. In another paper [19], behavior determinant-based cervical cancer early detection using NB and LR. [20] says that DT is often used to find the best way to classify a medical diagnosis .

In addition, the [21] is to classify breast cancer survival patterns using SVM, LR, and DT. More specifically, research comparing some of the methods mentioned above has revealed that SVM outperforms a large number of similar methods [22], [23]. In some studies, the expert system based on fuzzy logic was implemented to predict the responses to the treatment method in paper [24].

On the other hand, it is known that combining multiple classifiers, also called "ensemble classifier" often gives better results than a single classifier [25]. In the experiment in the paper [15], improvement of results from the NB algorithms was investigated using the combined ensemble method. This paper showed the improved accuracy of the Bagging of NB algorithm on Breast Cancer Wisconsin ( ) and Hepatitis ( ) datasets. In [26], single, boosted, and decision tree forest (RF) are used to detect breast cancer [26]. BDTs ( ) outperformed DT ( ). RF scored , beating DT ( ) and BDTs ( ).

The performance of ensemble models in medical diagnosis has not been extensively studied, with the exception of [27] and [15], which show that bagging and boosting learning perform better than a single model. The fact that the medical dataset is typically class-imbalanced is yet another factor that can make things more complicated. Therefore, judging the effectiveness of the prediction models based solely on their classification accuracy is insufficient [28].

Therefore, in this study, we aim to investigate the usefulness of ensemble SVM for medical diagnosis. In order to evaluate SVM ensemble model, we use classification accuracy, AUC, and classifier training time. So, the results of this study should make it easy for researchers in the future to choose the best algorithms for medical diagnosis.

Materials and Methods

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In medical data classification, the popular method for classification models is to build only a single strong classifier to predict labels from data. In recent decades, the ensemble method has been one of the main research directions in machine learning. It combines multiple learners in order to improve the performance of a single classifier [29]. Bagging and boosting are two different approaches to ensembles. While the boosting method will reduce both bias and difference, the bagging method will only reduce the difference between the base models [30].

Support Vector Machines.

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The support vector machine (SVM) is a method for classifying data that was made by Vapnik in the 1990s. It is a joint effort between researchers in statistics and machine learning [31]. The main idea behind the SVM algorithm is that it finds the best way to divide datasets into classes by finding the best hyperplane. Figure 1 shows the best hyperplane, which is the one that is the farthest away from the two categories of labeled points on either side. It can be found by maximizing the distance or margin between the supporting planes for each class.

Figure 1: Binary classification using a support vector machine

Figure 1. Figure 1: Binary classification using a support vector machine

The margin between these supporting planes is (where is the 2 - norm of the vector w). The quadratic programming of (1) and (2) are used by the standard SVM to endeavor to achieve these goals (2).

where C is a positive constant used to tune the margin and the error and a linear kernel function . Using the SVM model, a new data point x is classified as follows (3):

The SVM algorithm employs various kernel functions for non-linear classification task [4]. For nonlinear data, the radial basis function (RBF) is a popular kernel function. Kernel function are method for approximating multivariable functions using linear combinations of terms derived from a
single univariable function. The RBF function is given (4), where is specified by parameter gamma, must be greater than 0 .

Bagging of Support Vector Machines

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Bootstrap Aggregation (Bagging) is an ensemble method that combines base classifiers to form a final prediction [32]. Bagging employs bootstrapping estimates to estimate the sampling distribution of almost any statistic using random sampling techniques. To improve the performance of SVM using the RBF kernel for medical data, we propose using Bagging of SVM for medical data. Bagging of Support Vector Machines (denoted by BA-SVMs) constructs a collection of SVM classifiers (SVMs). The main goal of BA-SVMs is to create an ensemble by adding randomization to the SVM construction process and thereby reducing its variance. The Figure 2 shows a schematic diagram of the complete bagging process.

Figure 2. Bagging of Support Vector Machines (BA-SVMs)

Figure 2. Figure 2. Bagging of Support Vector Machines (BA-SVMs)

The idea of algorithm is simple and appealing:
(1) Given a training set . Random sampling is used to select n replacement individuals from the initial training set D to create the learning set Bootstrap T .
(2) train a SVM using RBF kernel on each and obtain a sequence of outputs .
(3) SVM classifiers that classify new individuals by using a majority vote (as follow (5)).

In medical data classification, learning classifying models face numerous challenges, including errors caused primarily by bias, noise, and variance [33]. In Breiman's paper [32], bagging is shown to be able to provide significant accuracy gains. He pointed out that the stability of the base learner is the key factor for the performance of this algorithm. Because bagging is primarily a variance-
reduction method, and since the overall error is the sum of bias and variance. SVM has a built-in mechanism for reducing variance: they look for the classifier with the highest margin among all potential linear separators. In our study, we propose tuning the BA-SVMs to introduce significant changes in the various learning sets while minimizing bias. In order to give the large margin model noise robustness for medical datasets, it is important to set the cost constant C, which trades off margin size and errors, to a high value (more than 100) in SVM learning tasks. Another key idea is reducing the number of samples to be drawn from the training set to train each base estimator. The random addition of discounts ranging from to (depending on the data set). This idea is a little bit bigger when C is high. This is mostly because the unbiased variance is smaller when C is high. The paper [34] says that the relative reduction of the unbiased variance is about , while the bias is mostly the same.

Gradient boosting of Support Vector Machines

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Boosting [35] has been one of the most important developments in classification problems in the last 20 years. The boosting technique is used to make a model with fewer mistakes because it focuses on improving the positive aspects and fixing the bad elements of a single model. Unlike with the bagging approach, the target of boosting is to make the prediction model more powerful by reducing its bias. The boosting models are designed from weak classifiers such as DT, LR, NB, and KNN. In this paper, we propose using the Gradient Boosting to improve accuracy of SVM with RBF kernel for medical data classification.

Figure 3: Gradient Boosting of Support Vector Machines (GB-SVMs)

Figure 3. Figure 3: Gradient Boosting of Support Vector Machines (GB-SVMs)

Figure 3 shows a diagram of the whole process of boosting. The main idea behind boosting is to keep adding new models to the ensemble sequentially. By starting with an SVM model, boosting attacks the trade-off between bias and variance. The algorithm improves performance by building more and more models, with each new SVM trying to fix where the last one went wrong the most. This means that each new SVM in the sequence will focus on the training rows where the previous SVM made the most wrong predictions. SVM classifiers use a weighted majority vote to decide how to put a new person, x , into a category.

In our algorithm, the learning procedure consecutively fits new classifiers to compute a more accurate estimate of the response error of the previous step. The main idea behind GB-SVMs is to build SVM models that are most similar to the loss function of the negative gradient of the whole ensemble. The loss function is to be optimized for classification with probabilistic outputs. In GBSVMs, the friedman-mse loss function is a modified kind of mse loss function, especially for boosting algorithms. It defines as (4), where N is the number of samples.

Boosting algorithm has been demonstrated that boosting can improve the predictive performance of weak learners like decision trees, but it does not improve the performance of strong learners like SVM. Using SVM as the base learner in boosting does not seem to offer significant advantages in terms of generalization error. Therefore, the main idea of GB-SVM is to tune the C and parameters are adjusted to the large margin solution of SVMs and can improve the generalization capacity of GB-SVMs against over-fitting.

Evaluation

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Figure 4 displays a flowchart of our model development process. The following steps make up the experimental process.

Figure 4. Experiment pipeline for preprocessing and classification

Figure 4. Figure 4. Experiment pipeline for preprocessing and classification

First of all, is used for completing missing values for some missing datasets [36]. The training set's n nearest neighbors' mean values are used to impute each sample's missing values. The next step is to transform the processed data. The processed data may contain attributes with a mixture of scales for various quantities. By bringing the scale to unit variance and lowering the mean, we have standardized the features. It is important to standardize a dataset because individual features that don't follow the rules for normally distributed data may not work well.

For each medical dataset, we construct twelve classification models. Firstly, we implement a baseline model, such as LSVM, SVM with an RBF kernel, KNN, DT, NB, and LR. RFs, BA-DTs, and GB-DTs are all types of ensemble models that use decision trees. We are interested in the classification performance of our proposal for medical data. Therefore, the bagging and gradient boosting of SVM are constructed to compare with the classification models (named BA-SVMs and GB-SVMs). Summary, the experiment is implemented with 12 classifiers on forty medical datasets to compare the predictive of the models.

In order to evaluate models, the accuracy measure (ACC) is utilized, which quantifies a classification model's performance as the number of correct predictions divided by the total number of predictions. In addition, the classification models also are evaluated by AUC (Area under the ROC Curve) because AUC is a more discriminating and statistically consistent measure than accuracy.

The paired Student's t-test might then be used to determine whether the difference in mean accuracy and AUC between the two models is statistically significant.

Experiments Setup

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Using the Scikit library, we built BA-SVMs and GB-SVMs in Python [37]. As starting points, we use algorithms from the Scikit library like KNN, LR, NB, RF, XGB, BA-DTs, and GB-DTs, as well as the very effective standard linear SVM [38]. The server used was an Intel Xeon(R) CPU PC running at 2.7 GHz and having 44 cores. The RAM was 32 GB .

Dataset description

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The following experiments were conducted using forty benchmark datasets as described in Table 1. were obtained from the Machine Learning Repository at the University of California, Irvine (UCI) [39]. In this table, the protocol evaluation is shown in Column 6. Stratified 10-fold and leave-one-out cross-validation (LOO) are used to evaluate the robustness of the estimates made with the classification models.

Table 1. Brief description of 40 medical datasets are used in this research

ID | Dataset | Number samples | Number features | Number classes | Protocal evaluation
1 | Lung Cancer | 32 | 56 | 3 | Loo
2 | Cervical Cancer Behavior Risk | 72 | 19 | 2 | Loo
3 | Cryotherapy | 90 | 6 | 2 | Loo
4 | Immunotherapy | 90 | 7 | 2 | Loo
5 | Breast_tissue | 106 | 9 | 6 | Loo
6 | Breast cancer | 116 | 9 | 2 | Loo
7 | Lymphography | 148 | 18 | 4 | Loo
8 | Hepatitis | 155 | 19 | 2 | Loo
9 | Hepatocellular carcinoma | 168 | 49 | 2 | Loo
10 | EEG signals (Planning Relax) | 182 | 12 | 2 | Loo
11 | Parkinsons | 195 | 22 | 2 | Loo
12 | Prognostic Wisconsin Breast Cancer | 198 | 33 | 2 | Loo
13 | Thyroid | 215 | 5 | 3 | Loo
14 | Parkinson (replicated acoustic features) | 240 | 45 | 2 | Loo
15 | Spect heart | 267 | 22 | 2 | Loo
16 | Spectf heart | 267 | 44 | 2 | Loo
17 | Statlog heart | 270 | 13 | 2 | Loo
18 | Breast cancer | 286 | 9 | 2 | Loo
19 | Heart failure | 299 | 12 | 2 | Loo
20 | Heart cleveland | 303 | 13 | 5 | 10-Folds
21 | Haberman's Survival | 306 | 3 | 2 | 10-Folds
22 | Primary tumor | 339 | 16 | 3 | 10-Folds
23 | Bupa Liver Disorders | 345 | 6 | 2 | 10-Folds

Tuning parameters

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The ensemble models of decision trees are trained from 50 to 200 trees. The algorithm uses k among 1, 3, 5, 7, 9. For DT, the Gini index is used to split a decision tree. LR needs to choose C parameters to find out the best C value for it. The C of LR and is used among . For a linear SVM, the cost constant C is tuned among . SVM using the RBF kernel has two hyper-parameters ( C or values). For C , we consider many cases in which all coefficients are constant and have the values ., respectively. The is selected in among . For the NB algorithm, we use Gaussian Naive Bayes to classify for all datasets.

The performance of BA-SVMs and GB-SVMs is determined by the number of SVMs and the RBF kernel parameters, which aids SVM in finding the optimal separating hyperplane in the feature space. In order to find the best hyper-parameters of BA-SVMs and GB-SVMs models, we tune the number of SVM of ensemble model (No. of SVM) and C, . We tried 25 to 200 SVMs to get the best results. In addition, we also tuned the RBF kernel's and C parameters for accuracy. For , its value is , and auto}. If is 'auto', uses number of features. For C , The cost constant C is tuned among .

Table 2 displays the top parameters. The finding best parameters of BA-SVMs and GB-SVMs in this paper are very benefit for the related studies. Table 2 show that the C constant to high value (more than 100) in BA-SVMs algorithms make improve the effective classification for medical dataset. Additionally, depending on the data set, the bootstrap process of BA-SVMs has decreased the number with samples of reductions ranging from 10 to . For GB-SVMs, the tuning C and show that this algorithm has the best accuracy with C contain is low value (less than 100 ) in algorithm.

Table 2: The best tuning parameters of BA-SVMs and GB-SVMs

ID | BA-SVMs | GB-SVMs | ID | BA-SVMs | GB-SVMs
C | γ | C | γ | C | γ | C | γ
1 | 150 | 1 | 10⁻² | 150 | 1 | 1 | 21 | 200 | 10 | 10⁻³ | 200 | 0,1 | auto
2 | 150 | 10² | 10⁻³ | 50 | 10² | Auto | 22 | 50 | 1 | scale | 50 | 0,01 | 0,05
3 | 200 | 10² | 10⁻² | 25 | 10² | 0,01 | 23 | 150 | 10 | 0,01 | 150 | 10² | auto
4 | 200 | 0,01 | Auto | 50 | 1 | 10⁻³ | 24 | 50 | 1 | 10 | 50 | 1 | 1
5 | 200 | 10 | Auto | 50 | 10 | auto | 25 | 50 | 1 | 10³ | 50 | 1 | auto
6 | 200 | 0,1 | 10⁻³ | 50 | 0,01 | 0,1 | 26 | 200 | 10 | 10⁻³ | 200 | 1 | 10⁻³
7 | 200 | 10² | 10⁻³ | 50 | 10² | auto | 27 | 50 | 10³ | 10⁻³ | 50 | 10 | 0,01
8 | 25 | 10 | 10³ | 200 | 10² | 10 | 28 | 150 | 0,1 | 10 | 150 | 10² | 10⁻³
9 | 200 | 10 | 0,01 | 50 | 0,01 | auto | 29 | 200 | 10² | 0,1 | 200 | 1 | 0,1
10 | 50 | 1 | 1 | 50 | 1 | 1 | 30 | 102 | 10² | auto | 10² | 1 | auto
11 | 50 | 10² | auto | 50 | 10 | auto | 31 | 200 | 1 | 0,1 | 200 | 10² | 0,01
12 | 50 | 10² | scale | 50 | 10 | auto | 32 | 150 | 10 | 0,01 | 150 | 0,1 | 0,1
13 | 50 | 10³ | 10⁻³ | 50 | 1 | 0,2 | 33 | 50 | 10 | auto | 50 | 1 | auto
14 | 50 | 10 | 10⁻³ | 50 | 0,1 | 0,01 | 34 | 50 | 10 | 0,01 | 50 | 1 | 10⁻³
15 | 50 | 10 | 10⁻³ | 50 | 0,01 | 0,1 | 35 | 50 | 1 | 0,01 | 50 | 10 | 0,1
16 | 150 | 10 | auto | 150 | 0,1 | auto | 36 | 200 | 10 | 10⁻³ | 200 | 0,1 | 10⁻³
17 | 200 | 1 | 10⁻³ | 50 | 0,1 | 10 | 37 | 150 | 10³ | 10² | 150 | 10² | 10³
18 | 50 | 10 | scale | 50 | 10 | 10 | 38 | 150 | 10³ | 0,01 | 150 | 10² | 10⁻³
19 | 200 | 1 | 1 | 50 | 10² | auto | 39 | 102 | 10³ | 10⁻³ | 10² | 10² | 10⁻³
20 | 200 | 10 | 0,01 | 50 | 1 | 10⁻³ | 40 | 102 | 10² | 10⁻³ | 10² | 10⁻² | 10⁻³

Classification Results

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First of all, Table 3 shows the mean ACC and AUC of classifying models on 40 medical datasets in the experiment. The models we proposed (BA-SVMs and GB-SVMs) did the best job of classifying when compared to other models. The results demonstrate that the GB-SVMs model, which had an ACC and AUC of and , respectively, attained the best classification performance. In addition, the BA-SVMs model also has better classification results than other classifiers, with an ACC of and an AUC of . As the classification results show, it is clear that BA-SVMs provide more accuracy than the individual SVM classifiers and base classifiers. In the classification models, the GB-SVMs algorithm has the highest accuracy compared to the others, while logistic regression and KNN have the lowest accuracy for close movement. In addition, the ensemble decision tree models include RFs, BA-DTs, and GB-DTs that improve the accuracy of simple decision trees. This shows that the ensemble approaches could enhance the performance of base classifiers. However, the contribution of this work shows that using the strong base classifier (SVM) in the ensemble method has better performance compared to the weak base classifiers (decision trees, decision stump) for medical data. The explanation of this result is the following:

A low C value (less than 100) smooths the decision surface for GB-SVMs, iteratively learning SVM classifiers, and after a SVM learner is added, the data weights are readjusted, a process known
as "re-weighting." Thus, future SVM learners focus more on the examples that previous SVM learners misclassified.

On the other hand, for BA-SVMs, a high value C (more than 100) aims at classifying all training examples correctly. This is reason lead to overfiting issue of the base classifer. However, the combining of predictions from multiple separate models handles this problem.

Table 3: The mean accuracy and AUC of twelve algorithms are compared

Models
LR
NB
KNN
DT
BA
DTs
GB
DTs
RF
XGB
LSVM
SVM
BA
SVMs
GB
SVMs

ACC
74.61
74.63
78.39
79.89
80.34
80.31
80.21
80.07
78.78
79.78
81.76
82.82

AUC
75.04
71.22
75.17
73.02
75.35
77.21
76.75
75.29
74.72
77.16
80.06
80.52

Furthermore, the mean ACC and AUC scores of models are illustrated in the graph in Figure 5. The bar chart shows the mean percentage of the classification results on 40 medical datasets. The chart shows the percentages in 12 algorithms, the BA-SVMs, the GB-SVMs, and other state-of-theart algorithms. As the diagram suggests, it shows that our proposed models are better than the ones that are already out there. As is observed, GB-SVMs have performance still better BA-SVMs in ensemble approaches. Ensemble decision tree models (RF, XGB, Bag-DTs, and GB-DTs) are better at classifying than algorithms like DT, NB, LSVM, and SVM, which are used alone.

Figure 5: Comparison of the mean Accuracy and AUC among twelve algorithms

Figure 5. Figure 5: Comparison of the mean Accuracy and AUC among twelve algorithms

Secondly, we investigated the performance of BA-SVMs and GB-SVMs in classification result details on 40 datasets. To evaluate classification models, most studies use an accuracy metric. However, in this work we are not only using accuracy but also AUC. The Area Under the Curve (AUC) is the measure of the ability of a classifier to distinguish between classes and is used as a summary of the ROC curve. Table 4 presents accuracy, and Table 5 shows AUC. Two tables clearly show the results of the 40 datasets. In two tables, the value of each row is the results of classifying 40 medical datasets. The best value is bolded, and this is the best model out of 12 models. Moreover, Table 6 summarizes the results of these statistical tests with paired student ratio tests. The significant results indicate excess p -values just below 0.05 and are reported in bold. The p -values higher than 0.05 ( p -value ) are not statistically significant.

For GB-SVMs, as we can see in Table 6, the performance of GB-SVMs and BA-SVMs allows them to produce significantly better performance (all p -values less than 0.05 ). In particular, the best
performances are obtained by GB-SVMs for classification (ACC as well as AUC). The classification results of GB-SVMs and BA-SVMs are compared with base classifiers, including KNN, NB, LR, DT, LSVM, and SVM. In detail, the GB-SVMs model outperforms the competitive models in terms of ACC and AUC performance measures. Table 4 also shows that it improves the accuracy mean of 3.37, 7.13, 7.15, 4.44, 2.98, and 1.97 percent points obtained by KNN, NB, LR, DT, LSVM, and SVM, respectively. In addition, it also enhances the AUC mean of KNN, NB, LR, DT, LSVM, and SVM, which is , and 2.9 percent points in Table 5.

For BA-SVMs, in terms of ACC performance metrics, the BA-SVMs algorithm obviously increases the mean accuracy of 3.37, 7.13, 7.15, 4.44, 2.98 , and 1.97 percent points compared to KNN, NB, LR, DT, LSVM, and SVM, respectively. In terms of AUC scores, BA-SVMs clearly outperform KNN, NB, LR, DT, LSVM, and SVM by 4.9, 8.84, 5.02, 7.04, 5.25, and 2.9 percent points, respectively. Remarkably, it becomes apparent that GB-SVMs and BA-SVMs outperform performance compared with single SVM models (see Tables 5 and 6). All p-values are less than 0.05, which results in statistically meaningful results. It shows that our proposed models have improved ACC and AUC over the single SVM model.

In addition, we compare GB-SVMs and BA-SVMs with the ensemble of decision tree models. It aims to explore the effectiveness of SVM in the ensemble learning method. In Figure 5, we see that GB-SVMs (ACC: 82,92%, AUC: 80,52%) and BA-SVMs (ACC: 81.76%, AUC: 80.06%) outperform RF (ACC: 80.21%, AUC: 76.75%), BA-DTs (ACC: 79.89%, AUC: 75.8%), GB-DTs (ACC: , AUC: ), and XGB (ACC: , AUC: ). In Table 6, the results of proper statistical analyses reveal that GB-SVMs and BA-SVMs revealed statistically significant comparisons with ensemble models using the decision tree (all p-values are less than 0.05 ).

Table 4: Accuracy classification results of twelve algorithms on 40 datasets

ID | KNN | NB | LR | DT | BA DTs | GB DTs | RF | XGB | LSVM | SVM | BA SVMs | GB SVMs
1 | 46.88 | 56.25 | 46.88 | 53.12 | 56.25 | 62.5 | 40.62 | 50 | 53.12 | 50 | 54.06 | 65.31
2 | 87.5 | 91.67 | 94.44 | 83.47 | 88.89 | 88.89 | 90.28 | 86.11 | 95.83 | 93.06 | 94.16 | 91.53
3 | 92.22 | 85.56 | 87.78 | 92.22 | 91.11 | 94.44 | 94.44 | 93.33 | 90 | 92.22 | 92.11 | 95.78
4 | 80 | 78.89 | 68.89 | 85.56 | 86.67 | 87.78 | 87.78 | 81.11 | 78.89 | 80 | 94.67 | 89
5 | 75.47 | 64.15 | 72.64 | 76.42 | 72.64 | 69.81 | 71.7 | 71.7 | 70.75 | 78.3 | 77.17 | 77.74
6 | 71.55 | 69.83 | 71.55 | 75.86 | 75.86 | 79.31 | 73.28 | 78.45 | 71.55 | 75 | 78.79 | 79.91
7 | 86.49 | 83.11 | 86.49 | 73.65 | 83.78 | 88.51 | 86.49 | 85.14 | 86.49 | 87.84 | 88.72 | 90.54
8 | 80 | 75.48 | 67.1 | 75.29 | 82.58 | 81.94 | 82.58 | 79.35 | 76.77 | 80.65 | 80.97 | 83.42
9 | 69.09 | 67.88 | 70.91 | 60.18 | 70.91 | 70.91 | 72.73 | 77.58 | 73.33 | 72.73 | 73.82 | 76.96
10 | 67.58 | 67.03 | 45.05 | 70.6 | 69.78 | 65.93 | 71.98 | 67.03 | 67.03 | 74.18 | 73.63 | 73.85
11 | 94.36 | 71.28 | 86.15 | 88.67 | 91.28 | 93.85 | 91.28 | 91.28 | 86.67 | 93.85 | 94.26 | 95.38
12 | 78.79 | 66.16 | 75.76 | 76.26 | 81.82 | 78.28 | 79.29 | 80.81 | 82.32 | 77.78 | 80.05 | 82.02
13 | 96.28 | 97.74 | 98.14 | 91.67 | 94.88 | 94.88 | 95.81 | 95.35 | 97.21 | 98.14 | 98.09 | 97.76
14 | 81.67 | 83.75 | 80.83 | 82.17 | 78.75 | 80.42 | 81.67 | 79.58 | 80 | 82.08 | 82.21 | 81.88
15 | 68.54 | 68.54 | 70.04 | 74.91 | 68.91 | 73.41 | 71.16 | 70.04 | 72.28 | 72.28 | 72.4 | 74.01
16 | 73.78 | 73.41 | 77.15 | 82.9 | 82.4 | 78.65 | 81.65 | 81.27 | 80.9 | 73.41 | 81.76 | 83.56
17 | 83.33 | 84.07 | 84.81 | 81.41 | 80 | 81.85 | 82.22 | 83.33 | 83.7 | 84.44 | 85.08 | 83.92

Table 5: AUC classification results of twelve algorithms on 40 datasets

ID
KNN
NB
LR
DT
BA
DTs
GB
DTs
RF
XGB
LSVM
SVM
BA
SVMs
GB
SVMs

1
60.46
67.34
61.47
60.95
67.68
71.88
56.13
63.38
66.56
60.84
64.07
73.74

2
87.5
89.92
93.28
80.58
90.53
88.66
89.81
84.81
94.45
91.18
94.96
92.24

3
92.26
86.62
88
92.22
91.06
94.44
94.44
93.43
90.62
92.45
92.36
95.72

4
70.59
65.85
64.2
78.47
82.62
85.9
88.75
71.47
65.48
71.38
91.75
91.78

5
84.75
78.25
83.05
85.45
73.22
80.93
82.33
82.06
81.58
86.65
85.95
86.14

6
72.91
70.43
71.65
79.46
75.74
73.03
73.13
78.73
71.26
74.92
80.14
80.21

7
89.76
79.18
93.53
62.69
73.51
91.12
74.98
74.33
80.73
93.58
94.04
92.11

8
68.1
55.91
62.29
60.94
73.38
72.89
73.62
67.11
52.3
72.07
84.18
79.17

9
67.15
66.18
69.5
57.02
69.13
69.09
71.39
76.31
71.99
71.07
72.37
75.07

10
44.47
40.17
42.87
54.27
54.15
51.28
64.86
55.42
58.89
86.72
86.52
81.43

Table 6. Statistical Tests for Comparing Classification Algorithms

Comparisons | p-values | Wins | Ties | Loses | p-values | Wins | Ties | Loses
Comparison of the performance of GB-SVMs & the base classifiers
GB-SVMs & KNN | 1.47e-7 | 38 | 1 | 1 | 2.43e-4 | 34 | 0 | 6
GB-SVMs & NB | 2.32e-8 | 36 | 0 | 4 | 1.43e-6 | 35 | 0 | 5
GB-SVMs & LR | 2.70e-7 | 36 | 0 | 4 | 7.58e-4 | 27 | 0 | 13
GB-SVMs &DT | 1.30e-5 | 34 | 1 | 5 | 5.66e-6 | 33 | 0 | 7
GB-SVMs & LSVM | 5.63e-5 | 35 | 0 | 5 | 7.28e-5 | 34 | 0 | 6
GB-SVMs & SVM | 4.95e-6 | 31 | 0 | 9 | 2.83e-4 | 30 | 0 | 10
Comparison of the performance of GB-SVMs &the ensemble models based DTs
GB-SVMs & RF | 1.15e-4 | 38 | 1 | 1 | 2.89e-5 | 37 | 0 | 3
GB-SVMs & XGB | 1.62e-7 | 38 | 1 | 1 | 7.59e-5 | 36 | 0 | 4
GB-SVMs& BA-DTs | 5.86e-01 | 39 | 0 | 1 | 4.18e-1 | 39 | 0 | 1
GB-SVMs& GB-DTs | 2.78e-8 | 39 | 0 | 1 | 1.66e-3 | 36 | 0 | 4
Comparison of the performance of BA-SVMs & the base classifiers
BA-SVMs & KNN | 2.18e-6 | 35 | 0 | 5 | 6.69e-4 | 31 | 0 | 9
BA-SVMs & NB | 7.31e-8 | 38 | 0 | 2 | 2.47e-5 | 34 | 0 | 6
BA-SVMs & LR | 4.16e-6 | 36 | 0 | 4 | 7.47e-3 | 30 | 0 | 10
BA-SVMs & DT | 3.19e-4 | 32 | 0 | 8 | 2.03e-4 | 32 | 0 | 8
BA-SVMs & LSVM | 1.23e-3 | 32 | 0 | 8 | 2.17e-3 | 30 | 0 | 10
BA-SVMs & SVM | 2.37e-4 | 33 | 1 | 6 | 3.90e-3 | 28 | 1 | 11
Comparison of the performance of BA-SVMs & the ensemble models of DTs
BA-SVMs & RF | 1.02e-2 | 28 | 0 | 12 | 9.03e-3 | 27 | 0 | 13
BA-SVMs & XGB | 4.29e-3 | 30 | 0 | 10 | 6.73e-3 | 26 | 0 | 14
BA-SVMs & BA-DTs | 1.38e-3 | 33 | 0 | 7 | 5.75e-03 | 30 | 0 | 10
BA-SVMs & GB-DTs | 1.10e-2 | 30 | 0 | 10 | 1.19e-2 | 27 | 0 | 13
Comparison GB-SVMs and BA-SVMs
GB-SVMs & BA-SVMs | 1.16e-2 | 28 | 0 | 12 | 5.29e-1 | 24 | 0 | 16

Finally, in the comparison between GB-SVMs with BA-SVMs, GB-SVMs is superior to BASVMs with 28 wins, 12 defeats, -value for using ACC metric. However, GB-SVMs is slightly superior to BA-SVMs with 24 wins and 16 defeats, -value (not significantly different) using AUC scores.

We can see that SVM ensembles perform better than single SVM classifiers in most cases, particularly SVMs with an RBF kernel function. There are many ensemble classifiers that can perform the best for all of the evaluation metrics in Table 6. These results allow us to believe that GB-SVMs and BA-SVMs efficiently handle medical data to improve medical diagnosis.

In addition, BA-SVMs employ a divide-and-conquer strategy by aggregating many SVM models, trained on small sub-samples of the training set. It helps total training time decrease significantly,
even though more models need to be trained. Training classifiers on sub-samples of size , results in an approximate complexity of . This reduction in complexity helps in dealing with large datasets and nonlinear kernels. Comparison of the computational times for training, GB-SVMs and BA-SVMs need time more than single SVM because these models train many SVM models. For BASVMs, this algorithm can run parallel with multiple cores. In our experiment, SVM need 0.86s to train, BA-SVMs only uses 0.27 s to train with 16 cores and GB-SVMs uses 6.23 s . However, in our opinion, time training of the suggested models is appropriate, particularly when the final prediction model yields the greatest results in terms of classification ACC and AUC.

Comparison with Related Works and Discussion

The original version of this paper is available on https://www.scientific.net/EI.4.47.pdf

In order to compare the result of our study with previous studies, we present some related studies that use the same datasets as our experiment. Also, to see how the proposed method compares to similar studies, Table 7 shows the proposed method compares to studies that used medical datasets. Remark, there are only compare to with studies without feature selection in the pre-processing data stage.

Table 7. Comparison with related works

Dataset | Researches | ACC and algorithms used in research | ACC of Our work
Cervical cancer | [19] | 87.5% (logistic regression) | 91.53%
Cryotherapy | [7] | 92.22% (decision tree), 94.44 (random forest), 95.56 (k nearest neighbor) | 95.78%
Immunotherapy | [40] | 85.6% (feedforward neural network) | 89%
Lymphography | [41] | 84.46% (MLP) 77.03% (J48) | 90.54%
Hepatitis | [3] | 84.07% (SVM) | 83.42%
Hepatocellular carcinoma | [42] | 72.1% (logistic regression) | 76.96%
EEG signals (Planning Relax) | [43] | 85.73% (fuzzy-rough trees) | 73.85%
Parkinsons | [44] | 95% (deep forest) | 95.38%
Thyroid | [8] | 94.4% (k-nearest neighbor) | 97.76%
Parkinson | [45] | 86.2% | 81.88%
SPECT heart | [46] | 71.12 % (SVM) | 74.01%
SPECTF heart | [47] | 74.11% (Linear SVM) | 83.56%
Statlog heart | [48], [49] | 94% (deep learning), 87.4% | 83.92%
Haberman's Survival | [50] | 75.18 % (L-perceptron) | 74.25%
Bupa Liver Disorders | [6] | 80% (random forest) | 74.84%
Chronic | [51] | 97% (deep ANN) | 99.8%
Arrhythmia | [52] | 66% (SVM), 72% (random forest) | 74.76%
Thoraric surgery | [53] | 85.53 (XGB), 81.89% (RF), 84.36% (SVM) | 85.32%
Diabetes sylhet | [54] | 96% (neural network) | 99.04%
Breast Cancer Wisconsin (Diagnostic) | [9] | 97%(SVM, decision jungle), 95% (decision tree) | 98.1%
Indian Liver | [55] | 71.87% (random forest) | 72.45%
Hepatitis C virus | [56] | 94.4% (KNN) | 93.6%
Soybean | [57] | 89.22% (SVM), 91.18%(ANN) | 95.47%

In Table 7, we present some studies related to medical data classification. Each row show information to compare between the related work and our models. The first column is the dataset name that is used in the experiment. The second column is a source reference for research. Their classification accuracy is shown in Column 3. The last column presents classification accuracy for our models (GB-SVMs or BA-SVMs). The values improved are bold in Table 7. These comparisons show that an ensemble of support vector machines can predict disease based on medical data with good accuracy for many types of complex diseases. However, for a few medical datasets, these proposed algorithms are not suitable due to their low classification accuracy compared to other algorithms.

Furthermore, Table 7 also shows the characteristics of each data mining modality. Despite all the work, no widely used method exists for organizing medical data. The most reasonable explanation is because the medical field demands extremely precise data, particularly a very low percentage of false negatives. However, there are general methods that can be applied to a wide range of data. Specialized methods for specific applications that take prior knowledge into account can lead to improved performance.

However, there are challenges, including data mining techniques, unbalanced data, noisy data, performance, and scalability. Therefore, choosing an appropriate approach to a classification problem can thus be a difficult decision, and medical data classification tasks can be improved. Since there hasn't been a lot of research on how to use data mining techniques to classify medical data, there is room for more research and interesting research opportunities.

Conclusion and Future Works

The original version of this paper is available on https://www.scientific.net/EI.4.47.pdf

In this research, we have suggested two methods for classifying medical data: GB-SVMs and BASVMs. The study aims to discover the best algorithms for medical data through experimentation and analysis on various datasets. In order to create an ensemble of classifiers that is more accurate than a single SVM model, our suggested method entails training several SVM classifiers in the techniques of bagging and gradient boosting. The results indicate that the GB-SVMs and BA-SVMs outperform the individual model, base classification models, and ensemble method-based decision trees on forty medical datasets.

Future research can use SVM classifiers to implement the additional ensemble learning methods of stacking and voting for the classification of medical data. Additionally, a hybrid model that combines feature selection and classification algorithms can be utilized to enhance the accuracy of predictions for medical datasets.

Appendix:

The original version of this paper is available on https://www.scientific.net/EI.4.47.pdf

Abbreviations

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The original version of this paper is available on https://www.scientific.net/EI.4.47.pdf

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Keywords Index

#

3D Printing Scaffold Tensile Strength of 3D Printing Scaffold Design Truncated Hexahedron for Tuberculosis Drug Delivery

C

Collection Efficiency Optimization of a Developed Multi-Cyclone Using Response Surface Methodology (RSM) to Control Fine Particulate Emission

E

Ensemble Learning A Novel Ensemble of Support Vector Machines for Improving Medical Data Classification

Epitope Dataset A Novel Epitope Dataset: Performance of the MCL-Based Algorithms to Generate Dataset for Graph Learning Model

F

Fine Particulates Optimization of a Developed Multi-Cyclone Using Response Surface Methodology (RSM) to Control Fine Particulate Emission

G

Graph Clustering A Novel Epitope Dataset: Performance of the MCL-Based Algorithms to Generate Dataset for Graph Learning Model

Graph Learning Model A Novel Epitope Dataset: Performance of the MCL-Based Algorithms to Generate Dataset for Graph Learning Model

I

Injectable Bone Substitute Tensile Strength of 3D Printing Scaffold Design Truncated Hexahedron for Tuberculosis Drug Delivery

M

Medical Data Classification A Novel Ensemble of Support Vector Machines for Improving Medical Data Classification

Multi-cyclone Optimization of a Developed Multi-Cyclone Using Response Surface Methodology (RSM) to Control Fine Particulate Emission

P

Palm Oil Mill Optimization of a Developed Multi-Cyclone Using Response Surface Methodology (RSM) to Control Fine Particulate Emission

Ps-mcl A Novel Epitope Dataset: Performance of the MCL-Based Algorithms to Generate Dataset for Graph Learning Model

R

Resonance; Pressure Canister; Recoil; Vibration; Mini-uavs Vibrational analysis of Mini - Unmanned Aerial Vehicles Due to Gun Recoil

Response Surface Methodology (rsm) Optimization of a Developed Multi-Cyclone Using Response Surface Methodology (RSM) to Control Fine Particulate Emission

S

Shot Blasting Machine; Cylindrical Roller Bearing; Thermal-structural Coupling; Damage; The Finite Element Finite Element Analysis of Damage of Cylindrical Roller Bearing of Main Shaft of Shot Blasting Machine

Support Vector Machines A Novel Ensemble of Support Vector Machines for Improving Medical Data Classification

T

Tensile Strength Tensile Strength of 3D Printing Scaffold Design Truncated Hexahedron for Tuberculosis Drug Delivery

Tuberculosis Tensile Strength of 3D Printing Scaffold Design Truncated Hexahedron for Tuberculosis Drug Delivery

Authors Index

A

Aengchuan, P. Artificial Intelligence Predictions Effect of Loading Rate, Crack Width and Crack Length Ratio on Mode I Fracture Toughness of PMMA

Aminatun, A. Tensile Strength of 3D Printing Scaffold Design Truncated Hexahedron for Tuberculosis Drug Delivery

Ariyarit, A. Artificial Intelligence Predictions Effect of Loading Rate, Crack Width and Crack Length Ratio on Mode I Fracture Toughness of PMMA

Azhari, A. A Novel Epitope Dataset: Performance of the MCL-Based Algorithms to Generate Dataset for Graph Learning Model

B

Budiatin, A. Tensile Strength of 3D Printing Scaffold Design Truncated Hexahedron for Tuberculosis Drug Delivery

F

Fu, X. Finite Element Analysis of Damage of Cylindrical Roller Bearing of Main Shaft of Shot Blasting Machine

H

Hasyimah, N. Optimization of a Developed Multi-Cyclone Using Response Surface Methodology (RSM) to Control Fine Particulate Emission

Hikmawati, D. Tensile Strength of 3D Printing Scaffold Design Truncated Hexahedron for Tuberculosis Drug Delivery

Huynh, P. A Novel Ensemble of Support Vector Machines for Improving Medical Data Classification

J

Jiang, Z. Finite Element Analysis of Damage of Cylindrical Roller Bearing of Main Shaft of Shot Blasting Machine

M

Musdholifah, A. A Novel Epitope Dataset: Performance of the MCL-Based Algorithms to Generate Dataset for Graph Learning Model

N

Nelson, N. Vibrational analysis of Mini - Unmanned Aerial Vehicles Due to Gun Recoil

Nguyen, V. A Novel Ensemble of Support Vector Machines for Improving Medical Data Classification

Norelyza, H. Optimization of a Developed Multi-Cyclone Using Response Surface Methodology (RSM) to Control Fine Particulate Emission

P

Pan, Y. Finite Element Analysis of Damage of Cylindrical Roller Bearing of Main Shaft of Shot Blasting Machine

Parastuti, F. Tensile Strength of 3D Printing Scaffold Design Truncated Hexahedron for Tuberculosis Drug Delivery

R

Rashid, M. Optimization of a Developed Multi-Cyclone Using Response Surface Methodology (RSM) to Control Fine Particulate Emission

S

Siva, P. Vibrational analysis of Mini - Unmanned Aerial Vehicles Due to Gun Recoil

Solihah, B. A Novel Epitope Dataset: Performance of the MCL-Based Algorithms to Generate Dataset for Graph Learning Model

W

Wiangkham, A. Artificial Intelligence Predictions Effect of Loading Rate, Crack Width and Crack Length Ratio on Mode I Fracture Toughness of PMMA

Widiyanti, P. Tensile Strength of 3D Printing Scaffold Design Truncated Hexahedron for Tuberculosis Drug Delivery

Y

Yuliatin, E. Tensile Strength of 3D Printing Scaffold Design Truncated Hexahedron for Tuberculosis Drug Delivery

Z

Zhang, T. Finite Element Analysis of Damage of Cylindrical Roller Bearing of Main Shaft of Shot Blasting Machine

Zhang, Y. Finite Element Analysis of Damage of Cylindrical Roller Bearing of Main Shaft of Shot Blasting Machine