Papers by Keyword: Optimization

Abstract: Additive Manufacturing (AM) is a technique in constructing components from a CAD model to a finished product. This is done by depositing molten material at a specific coordinate and height. This is done continuously until the finished product has been produced. Both FDM and PLA are well-known technology and material in the AM field. Optimizing the parameters will surely provide a good reach for hobbyists, researchers, and academicians. Optimization is a process concerned with the identification of the best possible value/condition for a certain parameter. Most research papers optimize a response/factor at a time given that less than five parameters are being studied. This paper aims to optimize three mechanical properties such as strength, elongation, and modulus of elasticity. This response was optimized through seven (7) benchmark parameters conducted in mixed levels (a combination of two and three levels). This paper was able to identify the optimum level per parameter, provide insight into the significant contributors affecting the target responses, and lastly, provide a contour plot to serve as a reference of AM end-users.

Abstract: The main objective of this work is to optimize welding parameters of AISI 430 FSS welds, focused to minimization of ferrite grains size using Taguchi’s design. Two input parameters of speed and welding current; were chosen to select the minimum grain size and to ascertain their effects on ferrite grain size. ANOVA method was used to evaluate the influence of varying factors on the overall quality of welds. Optimal combination of the parameters were be predicted by S/N analyses, it was accessed on employing an 80 A with 6mm/s. Experimental characterizations of optimum weld joint were performed by using tensile test assisted by image correlations, optical and electronic microscopy. As a result, welding speed had the main influence on grain size by 84.30%. Optimum welding parameter offered finest microstructure with low rate of martensite precipitates in both fusion zone and heat affected zone, and best combination of strength and ductility, it presented a homogeneous distribution of tensile stresses that caused a ductile fracture in base material. ,it is found that that optimized welding parameters permit to give greater resistance to corrosion, which exhibit a lower corrosion current, indicating that coarse ferrite grains are more susceptible to corrosion compared to fine grains.

Abstract: The current research focuses on the viability of rotating, single tubular brass electrodes undergoing shallow cryogenic treatment (at -140°C) before micro-drilling austenitic stainless steel SS316L with the electrical discharge machining process. In order to study and achieve a better rate of material removal and a lower rate of electrode wear, the Taguchi L18 experimental matrix representing the four variables, current, duty cycle, capacitance level, and gap voltage was examined. Regular tap water served as the dielectric fluid to uphold the sustainability concept of the machining experiments and an integrated hybrid approach incorporating CRITIC (criteria importance through inter-criteria correlation) weight determination method and MOORA (multi-objective optimization by the ratio analysis) was applied for decision making. The weight fractions (significance) for MRR and EWR were found to be 0.5532 and 0.4467, respectively and the MOORA method converted multiple objective parameters into a single objective function with weight fractions assigned to each of them. An ideal parameter combination highlighting the dominant significance of duty cycle, pulse current, capacitance level and gap voltage with corresponding values of 70%-18A-1-34V was obtained and the results were substantiated with relevant confirmation experiments. The highest MRR achieved is 10.0961 mm³/min and the lowest EWR is found to be 3.9640 mm³/min. Moreover, the electrode tip regions, the micro holes, and the surrounding workpiece surfaces were also thoughtfully scrutinized and contrasted using scanning electron micrographs (SEM), which validates the worth and significance of cryogenically frozen electrodes in successful micro-drilling of SS316L material.

Abstract: Electrical Discharge Machining (EDM) is a non-conventional thermal energy based erosive process, which primarily applied for machining hard materials. Material Removal Rate (MRR) and surface roughness are the response parameters used to characterize the dielectric nature of the machined surface in EDM process. Addition of ingredients in the dielectric fluid improves the properties of fluid for better machining of the samples. The dielectric fluid medium plays a key role in controlling the electrical discharge and heat absorption, thereby removes the debris and cools the work piece during the machining process. In the current study, comprehensive work is done by investigating the effect of different dielectric fluid medium on machining parameters of EDM process with the addition of different powders in the dielectric fluid, which results in high precise and better topography in the machined part surface. Addition of powders such as Titanium (Ti), Silicon (Si), Graphite (Gr), Copper (Cu) and Aluminium Oxide (Al2O3) in dielectric fluid increases the convection property in the work piece tool interaction with increase in the micro-hardness of material. This work analyses the performance study of Electrical Discharge Machining (EDM) of Monel 400 alloys, which can be improved by adding metallic powder into the dielectric medium. Material Removal Rate (MRR) is measured in the samples machined out of EDM process. In addition, Taguchi L27 Orthogonal Array is formulated for conducting machining in a sequential order to understand the implications of machined process parameters on the material removal rate over different dielectric mediums. It is found that the Aluminium oxide, graphite powder mix with EDM oil gives better material removal rate and less machining time. Furthermore, the introduction of Cu powders in the dielectric fluid provides better machinability response parameters. But it is preferable to parts with high slenderness ratio especially holes.

Abstract: Textile dyes waste can cause a big problem for the environment. Adsorption is a simple approach in treatment of textile dyes waste. On the other hand, the use of disposable adsorbents also creates production cost problems because they are less economic. Currently, research on adsorbents is forwarded to the use of biopolymers such as chitosan, chitin, and cellulose. This research studied the use of cellulose beads, made from cellulose acetate (CA) blended with polyethylene glycol 200 (PEG200), as adsorbent in removing cationic dye of methylene blue (MB). Adsorption performance of cellulose beads was evaluated and optimized under variation of adsorption conditions (pH, beads dose, dye concentration) and PEG200 content. Optimization was carried out by using response surface methodology (RSM) with a face-centered central composite design (FCCD) model. The results showed that the optimum condition was obtained at pH of 7, beads dose of 2 g/L, dye concentration of 20 mg/L for bead composition of CA/PEG200 (90/10). The optimum % dye removal predicted by the design model was 52.4706 %.

Abstract: This paper presents the problem of determining the optimal input process parameters of a Fused Deposition Modelling 3D printer for improved mechanical strength of the 3D printed objects. Polylactic Acid material tensile test and Izod impact test specimen are printed as per the ASTM standards. Various critical input parameters infill density, infill pattern, raster angle and number of contours are analysed on the material. The tensile and impact strengths were determined by conducting 16 experiments using a Tensometer for tensile test while a pendulum impact test is used for Izod impact test. Adaptive Neuro-Fuzzy Inference Systems (ANFIS) is used to train input and output data and optimal parameters are obtained for improved mechanical strength. The tensile strength and impact strength have been improved by 19.8% and 18.3% respectively with the optimal set of input parameters determined in the analysis.

Abstract: Considering that velocity of diffusion in a system solute-porous material-solvent depends on several factors, (among them, the concentration differential between “solute” in the porous material and “solute” in the solvent) the diffusion process will finish only when maximum entropy is achieved. Thus, the solute concentration will be equal in the matrix and in the solvent (equilibrium concept). On the other hand, if the velocity of diffusion depends on the differential of concentration, then, the amount of materials transferred per time unit (diffusion rate) will diminish as the process goes on. Moreover, when the final concentration of solute in the porous material is desired to be lower than that of the one-stage-equilibrium, then n-more stages must be added. Thus, the decision to choose a process with one or more stages, as well as the end point in each stage (as close or as far as equilibrium) will determine processing time and the use of other resources, i.e. amount of solvent, installation size, financial investment and so on. Therefore, the objective of this study is to develop a tool that helps to optimize these decisions by using a numerical approach.

Abstract: Electrostatic Discharge is a phenomenon that results from separating two dissimilar solid surfaces that were in contact. It results from the transfer of electrons from one surface to the other. Hence, one of the surfaces is positively charged, while the other surface becomes negatively charged. This phenomenon takes place during single point diamond turning of contact lenses polymers such as ONSI-56. Since higher electrostatic discharge adversely affects surface roughness, there is need to optimize electrostatic discharge machining parameters. The aim of this study is to develop an electrostatic discharge model and optimize the electrostatic discharge machining parameters during single point diamond turning of ONSI-56. Multiple regression has been utilized for model development and Genetic Algorithm (GA) has been used to optimize the model parameters. The GA toolbox in MATLAB is used for optimization in this study. In this study, cutting speed, depth of cut and feed rate are the model variables, while electrostatic discharge is the response variable. The regression model’s effectiveness has been evaluated by the R² value method. The model has an R² value of 88.29%, indicating that there is a strong collective significant effect among the control and response variables. Additionally, the results indicated that cutting speed and feed rate are the most significant predictors, while depth of cut is a slightly less significant predictor. The optimization process yields the following optimal values for cutting speed, feed rate, depth of cut and ESD, respectively: 200 rpm, 12 mm/min, 10 µm and 1,28 kV. An assessment of population size against objective function execution time has revealed that a population size of 500 has the shortest execution time of 14.23 seconds. The results have revealed that the optimization technique (GA) is efficient in ESD process optimization during single point diamond turning of ONSI-56.

Abstract: In times when companies must respond efficiently to market demands, reverse engineering plays a fundamental role. Although design processes are commonly developed with digital workflows, in reverse engineering different and independent phases with bottlenecks between them are involved. The present work addresses the challenge of establishing a continuous and efficient data flow between the three-dimensional digitized data obtained with 3D scanning and the automatic generation of NC toolpaths. A methodology is developed for the generation and optimization of NC toolpaths directly from the 3D point cloud data obtained through the three-dimensional scanning of pre-existing geometries. The methodology consists of an algorithm developed with Grasshopper, a script for visual programming in Rhino’s interface. It does not only attempt to reconstruct the three-dimensional geometry of the scanned part but also, it directly generates the tool paths and optimizes them with evolutionary optimization algorithms that are integrated in the methodology. A case study is developed for TMU-SIO TOWGTAI machining center with the proposed methodology. Finally, the obtained results and the efficiency of the methodology are analyzed and presented.

Abstract: This study focus is on the physical solvent-based CO₂ capture process from an integrated Gasification Combined Cycle (IGCC) power plant, followed by a comparison of Dimethyl ether of polyethylene glycol (DEPG) solvent and methanol solvent, which is based on traditional technology and has a 95% CO₂ purification requirement. After achieving the optimal condition, we would compare which solvents are suitable for this system. This procedure was simulated in Aspen HYSYS. Additionally, this study examined the effects of factors that provide the CO₂ capture cost per ton ($/tCO₂ captured). The response surface approach is used to get the optimal result. In the case of the optimal condition of DEPG solvent, the inlet temperature of fuel gas is – 2 °C, the stripper of inlet temperature is at 67 °C, the pressure of the absorber is at 2,994 kPa, and the stage of absorber appears to be 12 stages. As a result, the lowest DEPG solvent process cost is $189.63 per ton of CO₂ collected. The minimum cost of CO₂ capture under the optimal condition of methanol solvent, where the temperature of the absorbent and fuel gas is – 8 and – 20 °C, the stripper inlet temperature appears to be at – 6 °C, and the concentration of the absorbent is 88.85 wt.% in methanol solvent, is then $19.21 per ton of CO₂ captured.