Engineering Innovations Volume 15

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Volume 15 of
Engineering Innovations
ISSN print 2813-0995
ISSN web 2813-1002

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Table of Contents

Analysis of the Dislocation Structure, Kinetics and Dynamics in the High-Entropy Alloy

Vasilij Natsik, Yuri Semerenko, Nikolai Galtsov, Diana Hurova, Viktor Zoryansky, Elena Tabachnikova, Tamara Bednarchuk, Peter Zinoviev

Portable VIS-NIR Spectrophotometer for Detecting Adulteration of Minced Beef and Chicken Using AS7341 Sensor with PCA Method

Humaidillah Kurniadi Wardana, Endarko Endarko, Agus Muhamad Hatta

Fault Detection in Spur Gears Through Vibration Signal Analysis and Machine Learning Techniques

Muhammad Talha Asif, Shahzad Ahmad, Asif Khan, Sohail Malik

Development of an loT-Enabled Smart Home Automation System

M.N. Afnan Uda, Uda Hashim, M.N.A. Uda, Ahmad Haziq Zulkifli, Widyastuti Widyastuti, Retno Asih, Yuri Pamungkas, Nurul Hulwani Ibrahim

IoT-Based Smart Energy Management for Smart Grids Applied in a Home

Abdullah Mushtaq, Wasim Ahmad

Impact of Artificial Intelligence on Management Control Processes

Kai Nobach, Lily Petriashvili

Keywords Index

Authors Index

The original version of this eBook is available on https://www.scientific.net/book/engineering-innovations-vol-15/978-3-0364-1968-8

Engineering Innovations, ISSN: 2813-1002, Vol. 15, pp 1-8

doi: 10.4028/p-V4p5sx

© 2025 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2025-07-21

Revised:
2025-10-06

Accepted:
2025-10-06

Online:
2025-11-18

Analysis of the Dislocation Structure, Kinetics and Dynamics in the High-Entropy Alloy

The original version of this paper is available on https://www.scientific.net/EI.15.1.pdf

VASILIJ Natsik , YURI Semerenko , NIKOLAI Galtsov , DIANA Hurova , VICTOR Zoryansky , ELENA Tabachnikova , TAMARA Bednarchuk , PETER Zinoviev

B. Verkin Institute for Low Temperature Physics and Engineering of the National Academy of Sciences of Ukraine, 47 Nauky Ave., 61103 Kharkiv, Ukraine

Institute of Low Temperature and Structure Research, Polish Academy of Sciences, P.O. Box 1410, 50-950 Wroclaw, Poland

natsik@ilt.kharkov.ua, semerenko@ilt.kharkov.ua, galtsov@ilt.kharkov.ua, hurova@ilt.kharkov.ua, zoryansky@ilt.kharkov.ua, tabachnikova@ilt.kharkov.ua,

Keywords: high entropy alloy; acoustic relaxation; plastic deformation, X-ray structural analysis, dislocation model

Abstract

A theoretical analysis of low-temperature plastic deformation processes and acoustic relaxation in the high-entropy alloy has been conducted. Within the framework of proposed dislocation model it was established the key types of dislocation defects in the alloy's lattice structure; types of barriers that hinder the movement of dislocation lines (strings); and mechanisms of thermally activated movement of various dislocation line elements through these barriers at room and low temperatures. Using this model, quantitative estimates have been derived for significant dislocation characteristics and their interaction with barriers, such as the distance between local obstacles in the slip plane , the Peierls stress for dislocations in an easy slip system , and more. Additionally, an estimated speed of sound based on the proposed model aligns well with the direct experimental data. The empirical estimates for the energy per unit length of a dislocation and the linear mass density are consistent with modern continuum dislocation theory. A detailed examination of the structure of the alloy was carried out using X-ray diffraction and Energy Dispersive Spectroscopy techniques. Numerical estimates of the dislocation density were obtained through Williamson-Hall analysis of X-ray diffraction patterns. It was found to correlate with the estimates overall length of dislocation segments per unit volume which effectively interacts with elastic vibrations of the sample , as determined from acoustic relaxation measurements.

Introduction

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High-Entropy Alloys (HEAs) are metallic systems consisting of five or more elements with nearly equiatomic concentrations. These alloys exhibit enhanced mixing entropy and possess an advantageous balance of strength and ductility, along with exceptional resistance to thermal and mechanical effects.

One of the representative HEAs with an fcc lattice is [1]. Its mechanical and acoustic properties at temperatures below 300 K have been studied in detail in [1], [5]-[9]. In this paper, we consider the relationship between the properties analyzed in [5]-[8]. We consider the application of the fundamental principles of modern dislocation theory to interpret these properties. Our study uses data from X-ray structural analysis, resonance mechanical spectroscopy [8], and the active deformation method [7].

Studied Samples

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The ingot of the multicomponent high-entropy alloy was obtained by remelting high-purity elements on a copper hearth with water cooling in an arc furnace, using a nonconsumable tungsten electrode in a purified argon atmosphere. To ensure a uniform distribution of elements, the ingot was subjected to five remelting cycles, periodically flipped on the hearth.
The electron microscopy imaging of the sample and its elemental composition were studied using Field Emission Scanning Electron Microscope (FE-SEM) FEI Nova NanoSEM 230 (FEI Company, Hillsboro, USA) along with energy dispersive X-ray spectrometer EDAX Genesis XM4 (EDAX Inc., Mahwah, USA). Fig. 1 shows the results of determining the averaged elemental composition of the alloy under study.

Fig. 1 The EDS patterns for the high-entropy alloy in state (II). The transition from structural state (I) to state (II) (annealing of the sample) does not change its chemical composition as a whole.

Fig. 1. The EDS patterns for the high-entropy alloy in state (II). The transition from structural state (I) to state (II) (annealing of the sample) does not change its chemical composition as a whole.

The metallographic analysis [6] reveals that the microstructure of the alloy exhibits a characteristic dendritic pattern, consisting primarily of a dense body of dendrites as the main structural component, along with interdendritic spaces. Data of scanning electron microscopy using an X-ray microanalyser show that the elemental composition of dendrites and interdendritic spaces is significantly different [6].

The alloy was examined in two structural states: (I) the as-cast coarse-grained state and (II) after high-temperature annealing in vacuum at 1250 K for 6 hours. After annealing, the samples were cooled together with the furnace (the cooling rate to 770 K was 5 degrees/min). It was established that annealing leads to a significant change in the alloy structure and the chemical composition of the structural regions [6], but does not change the chemical composition of the sample as a whole.

Dislocation Structure

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The X-ray diffraction (XRD) patterns of the high-entropy alloy ingot were studied in state (II). XRD measurements were carried out using a PANalytical X'pert Pro diffractometer (PANalytical B.V., Almelo, Netherlands) at the Institute of Low Temperatures and Structural Research of the Polish Academy of Sciences. XRD patterns were recorded at room temperature in Bragg-Brentano geometry over range: from to , with a anode radiation. Numerical estimates of the average value of dislocation density and the average value of coherent scattering regions size were obtained through WilliamsonHall analysis [10] of XRD patterns (Fig. 2). Such a small value of in a coarse-grained annealed sample may indicate the presence of a large number of low-angle boundaries.

Fig. 2 Williamson-Hall plot for the specimens of the high-entropy alloy in state (II). is the X-ray wavelength; is a instrument-corrected physical value of the Full Width at Half Maximum (FWHM) of Bragg peak.

Fig. 2. Williamson-Hall plot for the specimens of the high-entropy alloy in state (II). is the X-ray wavelength; is a instrument-corrected physical value of the Full Width at Half Maximum (FWHM) of Bragg peak.

Acoustic properties

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The temperature dependence of internal friction and dynamic Young's modulus was investigated in [8] using the mechanical resonance spectroscopy method. The approach for these experiments was detailed in [11]. For state (I) the temperature dependencies and do not exhibit any notable features, such as relaxation resonances. However, the transition to state (II) results in the emergence of the acoustic absorption peak (Fig. 3).

It has been established that the dependence recorded in experiments can be divided into resonant and background components:

here is the limit value of the module at ; coefficient depends on the material and the vibration mode under study; is the Einstein temperature of the material under study. This relation allowed us to obtain an estimate for the Einstein temperature.

Fig. 3. Temperature dependences of the acoustic characteristics of the alloy for two structural states [8]: a) temperature dependence of dynamic Young's modulus , - - state (I), - - state (II); b) temperature dependence of internal friction - state (I), - - state (II).

Fig. 3. Temperature dependences of the acoustic characteristics of the alloy for two structural states [8]: a) temperature dependence of dynamic Young's modulus , - - state (I), - - state (II); b) temperature dependence of internal friction - state (I), - - state (II).

In previous works [8], [1] we came to the conclusion that the peak at corresponds to the thermally activated separation of dislocation segments from point defects. The obtained values of activation energy , effective oscillation period and ( is the effective specific contribution of an individual relaxer with concentration ) are typical for Koiwa-Hasiguti relaxers in fcc metals. For Koiwa-Hasiguti relaxers ( is the length of dislocation segments with volume density which breaking away from an individual point defect) [12]. By making assumption , we derive an estimate for the contribution of one relaxer to internal friction and decreasing of the elastic modulus. Let us consider that small atomic clusters, spaced approximately a few nanometers apart, serve as local pinning centres for dislocation segments: they are recorded in state (II) by electron microscopy methods [4]. Then, when estimating the length , we can take the twice distance between clusters 20 nm . This value is consistent with our estimate for coherent scattering regions size and corresponds to the volume density of relaxers .

The dislocation density (overall length of dislocation segments per unit volume, which effectively interact with elastic vibrations of the sample) yields the following estimate .

It was also established that the resonant component of acoustic relaxation in state (II) contains both the main relaxation resonance (peak at ) and its satellite at , localized on the left slope of the main peak. Outlined in [8] statistical and thermal activation analysis leads us to the conclusion that the peak is due to Seeger relaxation with parameters: .

The motion equation for a dislocation string with linear mass density in a sinusoidal first-kind Peierls relief admits soliton solutions in the form of kinks with the width , mass and energy ; their properties was determined by the parameters of the potential and dislocation (here represents the energy per unit length of a dislocation within the continuum approximation, also known as linear tension; is the magnitude of barriers and is the critical stress for the first-kind Peierls relief). The period of natural oscillations of a rectilinear segment in the relief valley is and is the characteristic value of the speed of transverse sound vibrations in the crystal.

The average time of thermally activated nucleation of kink-antikink pairs on a straight segment of a dislocation line is described by the Arrhenius law ( is the Boltzman's constant) with activation energy and attempt period . The interaction of elastic vibrations - shear stress component in the slip plane) with such a process is one of the mechanisms of relaxation resonance [13]. It was established [1] that the discussed resonance in the studied HEA is caused by the Seeger process, therefore [14].

The parameters required for further evaluation of the studied HEA are as follows: Burgers vector , period of Peierls relief in the direction of easy sliding . [7]; density [6], shear modulus , Poisson's ratio 0.2 [2]. It leads to the following estimates: . .

The kink mass can be compared to the average atomic mass of the alloy under study, its width to the minimum interatomic spacing , and the Peierls critical stress to the shear modulus .

The obtained estimate aligns well with the available direct experimental data [2], [3].

Mechanical Properties

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Mechanical testing of the alloy was conducted across a broad temperature range from 0.5 K to 300 K . The methodology for analyzing mechanical properties using the active deformation technique at a constant rate is outlined in [7]. The experimental results for both structural states (I) and (II) are shown in Fig. 4 at a given strain rate . The work [1] shows that stage of linear strain hardening occurs at deformation , therefore for this alloy stress should be considered as the yield stress. Previous studies have shown that the plastic deformation of the alloy under investigation is governed by the conservative motion of complete dislocations within the typical for fcc crystals slip system [9]. The experimentally observed strong temperature dependence of suggests that plastic deformation is thermally activated, with both Peierls barriers and local barriers potentially acting as controlling factors in dislocation movement. In the first case, the fundamental mechanism of plastic deformation involves the thermally activated nucleation of a paired kink on a dislocation segment within the Peierls relief, while in the second case, it consists of the thermally activated overcoming of a local barrier by a dislocation segment.

Fig. 4. Diagrams of compression deformation of the alloy for two structural states (I) and (II) in coordinates at different deformation temperatures [1].

Fig. 4. Diagrams of compression deformation of the alloy for two structural states (I) and (II) in coordinates at different deformation temperatures [1].

Through the analysis of low-temperature acoustic resonances in the studied HEA, it was determined that in it the Peierls stress for dislocations in the easy slip system is significantly less than the low-temperature values of the yield strength . As a result, the Peierls relief has no substantial impact on the kinetics of low-temperature plastic deformation of this material; rather, it is governed by a sufficiently high volume density of local barriers.

Clusters of atoms from one of the alloy's constituent elements can serve as structural inhomogeneities that influence thermally activated plastic deformation in HEAs. Several neighbouring atoms of an element with a relatively large atomic radius generate local distortions in the crystal lattice, posing a considerable barrier to dislocation movement. Experimental observations in [4] have identified such clusters, with characteristic distances between them measuring several nanometers, i.e. for them . These estimates also practically coincide with the estimates we obtained for the size of coherent scattering regions and the twice distance between clusters .

Thermal activation analysis [15] of experimental results make it possible to obtain estimates for the parameters of local barriers and the distances between them in slip planes: density of local obstacles controlling the movement of dislocations , which corresponds to the distance between local obstacles in the sliding plane and the length of straight lines segments of dislocation lines located in the valleys of first kind Peierls relief 10 nm . This value is in good agreement with the estimates obtained from the study of acoustic properties and X-ray structural studies.

Conclusion

The original version of this paper is available on https://www.scientific.net/EI.15.1.pdf

A deep physical analysis of the mechanical, acoustic and structural properties of the alloy was performed. Quantitative estimates of various parameters of the proposed model were obtained, consistent with the experimental data of other authors. Thus, the proposed dislocation model does not simply describe the behavior of dislocations in the material under study, but links acoustic properties, thermodynamics and microstructure into a single picture.

It is shown that the structural inhomogeneities observed in the studied alloy at the atomic level (clusters of atoms with a large radius) actively influence the mechanical and acoustic properties of the material.

Based on thermal activation analysis of the process of macroscopic active plastic deformation, estimates were obtained of the density of local obstacles controlling the movement of dislocations ; as well as the distance between local obstacles in the slip plane ; and the length of straight lines segments of dislocation lines located in the valleys of first kind Peierls relief . This distance turned out to be the same as the length dislocation segments, the separation of which from point defects causes the anomaly of acoustic absorption at . This empirical estimate aligns with the experimentally observed distances between clusters of atoms with a relatively large atomic radius in [4], which induce local distortions in the crystal lattice and serve as substantial barriers to dislocations.

Quantitative estimates have been made for the key characteristics of kinetic a dynamic of dislocations:

The Einstein temperature of the material under study is .

An estimate of the overall length of dislocation segments per unit volume was obtained within the Koiwa-Hasiguti model for the relaxation process of sound absorption at . The obtained value is less than the value of overall dislocation density obtained by the X-ray diffraction analysis. This aligns with the notion that a substantial fraction of dislocations are concentrated at grain boundaries, while only those segments situated within grains and oriented favourably relative to the propagation direction of sound waves can efficiently engage with the cyclic deformation of the sample.

The estimate for the speed of sound , obtained in the continuum approximation of a dislocation string within the framework of the Seeger relaxation model of the sound absorption peak at is in good agreement with the direct experimental data of the works [2], [3].

The empirical estimates obtained by analyzing the absorption peak for the energy per unit length of a dislocation and the linear mass density do not contradict their estimates in the continuum theory of dislocations and .

Through the analysis of low-temperature acoustic resonances in the studied HEA, it was determined that in it the Peierls stress for dislocations in the easy slip system is significantly less than the low-temperature values of the yield strength . .

Acknowledgments

The original version of this paper is available on https://www.scientific.net/EI.15.1.pdf

The authors thank Dr. Damian Szymański for conducting alloy elemental composition measurement.

This work was partly supported by the NRFU (Grant 2023.03/0012); Projects No. 0122U001504 and No. 0124U000272 NAS of Ukraine and internship within the framework of scientific cooperation between the National Academy of Sciences of Ukraine and the Polish Academy of Sciences.

References

The original version of this paper is available on https://www.scientific.net/EI.15.1.pdf

Y. Semerenko, V. Natsik, E.D. Tabachnikova, Y. Huang and T.G. Langdon, Mechanisms of Low-Temperature Dislocation Motion in High-Entropy Alloy, Metals 14 (2024), 778. DOI: 10.3390/met14070778

O.S. Bulatov, V.S. Klochko, A.V. Korniyets, I.V. Kolodiy, O.O. Kondratov, T.M. Tikhonovska, Low temperature elastic properties of high-entropy alloy, Funct. Mater. 28 (2021) 492-496. DOI: 10.15407/fm28.03.492

V.S. Klochko, A.V. Korniyets, I.V. Kolodiy, O.O. Kondratov, V.I. Sokolenko, V.I. Spitsyna, T.M. Tykhonovska, and N.A. Yayes, Ultrasonic Investigation of High-Entropy Alloy at Low Temperature, Metallofiz. Noveishie Tekhnol. 45(4) (2023) 523-535. DOI: 10.15407/mfint.45.04.0523

E.J. Pickering, H.J. Stone, N.G. Jones, Fine-scale precipitation in the high-entropy alloy , Mat. Sci. Eng. A645 (2015) 65-71. DOI: 10.1016/j.msea.2015.08.010

M.O. Laktionova, O.D. Tabachnikova, Z. Tang, P.K. Liaw, Mechanical properties of the highentropy alloy Al 0.5 CoCrCuFeNi at temperatures of , Low Temp. Phys. 39 (2013) 630-632. DOI: 10.1063/1.4813688

Yu.O. Semerenko, O.D. Tabachnikova, T.M. Tikhonovska, I.V. Kolodiy, O.S. Tortika, S.E. Shumilin, and M.O. Laktionova, Temperature Dependence of the Acoustic and Mechanical Properties of Cast and Annealed High-Entropy Alloy, Metallofiz. Noveishie Tekhnol. 37(11) (2015) 1527-1538. (in Russian)

E.D. Tabachnikova, M.A. Laktionova, Yu.A. Semerenko, S.E. Shumilin, A.V. Podolskiy, M.A. Tikhonovsky, J. Miskuf, K. Csach, Mechanical properties of high entropy alloy in different structural states in temperature range , Low Temp. Phys. 43(9) (2017) 1108-1118. DOI: 10.1063/1.5004457

Yu.A. Semerenko and V.D. Natsik, Low temperature peak of internal friction in high entropy alloy, Low Temp. Phys. 46 (2020) 78-86. DOI: 10.1063/10.0000367

V.D. Natsik, Yu.O. Semerenko, E.D. Tabachnikova, Dislocation mechanisms of acoustic relaxation and plastic deformation of a high-entropy alloy under moderate to deep cooling conditions: Experiment and theory (Review), Low Temp. Phys. 51 (2025) 282-299. DOI: 10.1063/10.0035810

L.I. Mirkin, Handbook of X-Ray Analysis of Polycrystalline Material, Consultants Bureau, New York, 1964. 731 pp.

V.D. Natsik, Yu.A. Semerenko, Dislocation mechanisms of low-temperature acoustic relaxation in iron, Low Temp. Phys. 45(5) (2019) 551-567. DOI: 10.1063/1.5097366

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The original version of this eBook is available on https://www.scientific.net/book/engineering-innovations-vol-15/978-3-0364-1968-8

Engineering Innovations, ISSN: 2813-1002, Vol. 15, pp 9-20

doi: 10.4028/p-jSMi1U

© 2025 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2024-12-12

Revised:
2025-05-15

Accepted:
2025-05-16

Online:
2025-11-18

Portable VIS-NIR Spectrophotometer for Detecting Adulteration of Minced Beef and Chicken Using AS7341 Sensor with PCA Method

The original version of this paper is available on https://www.scientific.net/EI.15.9.pdf

Humaidillah Kurniadi Wardana , Endarko and Agus Muhamad Hatta

Department of Physics, Faculty of Science and Data Analytics, Institut Teknologi Sepuluh Nopember (ITS), Kampus ITS, Sukolilo, Surabaya 60111, Indonesia

Department of Physics Engineering, Faculty of Industrial Technology and System Engineering, Institut Teknologi Sepuluh Nopember (ITS), Kampus ITS, Sukolilo, Surabaya 60111, Indonesia

Electrical Engineering Study Program, Faculty of Engineering, Universitas Hasyim Asy'ari, Tebuireng, Cukir-Diwek, Jombang 61471, Indonesia

a*humaidillahwardana@unhasy.ac.id (corresponding author), endarko@phys.its.ac.id, emhatta@ep.its.ac.id

Keywords: AS7341 Sensor, Beef, Chicken, Pork, PCA, Spectrophotometer.

Abstract

The most vulnerable food products related to halal issues are in the form of mixing beef and chicken meat with pork, which has a physical resemblance if not carefully considered. The rise of meat adulteration is often found due to high demand and high prices. For this reason, a fast, effective, and low-cost meat adulteration detection tool is needed. Detection of beef and chicken adulteration in this study was carried out using a VIS-NIR spectrophotometer from an AS7341 multispectral sensor equipped with an LED light source and 11 channels to read the reflection of meat light in the near light and near infrared ranges, raspberry pi as a microcontroller, data displayed on an LCD stored in CSV form. The results of sensor response patterns formed in beef, chicken, pork, mixed beef-pork, and minced chicken-pork mixed meat show different characteristics. Then to clarify the characteristics of each meat, the results of the sensor response were analyzed using the Principle Componen Analysis (PCA) method. The results of data reduction from PCA projections through Principle Component 1 and Principle Component 2 regions are able to detect the presence of pork mixture in beef and chicken. The results of the PCA score plot on beef, pork and cow-pig mixture the percentage of PC1 is and PC2 is while on chicken, pork and chicken-pig mixture the percentage of PC 1 is and PC 2 is . The results of this study show the great potential of using a portable spectrophotometer using the AS7341 sensor whose results are analyzed using the PCA method to detect adulteration of minced beef and chicken.

Introduction

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Meat is a very nutritious food ingredient and has high nutrients such as protein, vitamins, minerals, essential amino acids, and fatty acids . The most vulnerable food product related to halal issues is beef adulteration by mixing beef with pork which has a physical resemblance if not carefully observed [3]. The rise of meat adulteration is usually found before major holidays due to high demand and prices. For this reason, a fast, effective, and flexible meat counterfeiting detection tool is needed.

Usually detecting the authenticity and halalness of food products with meat-based ingredients using protein-based methods, namely Enzyme Linked Immunosorbent Assay (ELISA) [4-7], with gas chromotography techniques [8-10], mass spectrometer for fatty acid analysis [11-13], DNA-based methods using real time Polymerase Chane Reaction (PCR) and multiplex PCR [14]. However, these methods require expensive reagents, complicated sample preparation, cumbersome, cannot be used in various places, and require a long time.

The presence of pig elements can also be detected physically using spectroscopic testing methods. The type of testing with spectroscopy that is developing at this time is FTIR (Fourier Transform Infrared) spectroscopy. FTIR spectroscopy was chosen because it is based on the interaction of analytes with electromagnetic radiation in the infrared region, is fingerprinted, that is, no two different
compounds have the same IR spectrum [15], non-destructive analysis techniques, little sample used, easy and sensitive sample preparation [16], and there is no need for special treatment of samples [17]. FTIR spectroscopy with multivariate analysis and M-SVM classification successfully detected lard adulteration in beef, lamb, and chicken blends [18]. FTIR spectroscopy with chemometric combination identified beef meatballs from pork [19], wild boar meat [15], rat meat [20], dog meat [21], beef sausage from wild boar meat [22], beef jerky from pork meat [23], skin crackers from pork skin [24].

In general, spectroscopic devices used to detect pig content in food products use laboratory-scale spectroscopy. Where this device has a very expensive price, its operation requires special skills, the size of the device is usually large, and it is not practical if used outside the laboratory [25]. One of the spectroscopy-based sensors that is cheap, easy to use, easy to find on the market, and easy to apply in the field is the AS7341 sensor. The AS3741 sensor is a reflectance-based sensor that has 11 channels consisting of 8 UV channels, 1 NIR channel, 1 non-filtered channel, and 1 flicker detection channel [26]. The AS7341 sensor is widely applied in the health sector as a non-invasive blood sugar level measurement tool [27-28]. The agricultural field as a photosynthesis measuring instrument for horticultural lighting [29], photosynthetically active radiation (PAR) measuring instrument [30]. In the field of water as a remote sensing measuring instrument in monitoring the aquatic environment (optical properties of water) [31]. Land field as a soil test tool to predict the level of NH4+ and NO3- in soil [32]. Measuring air pollution and air radiation [33,34].

Based on previous research to date, there is still no research on the use of the AS7341 sensor to detect counterfeiting and the safety of meat. For this reason, in this study, the manufacture of a portable VIS-NIR spectrophotometer was made by utilizing the AS7341 spectroscopic sensor as a detector equipped with a white LED as a light source, raspberry pi 4B as a microcontroller, the display results of spectra measurements and classification are displayed on an HDMI LCD. This tool uses Principel Componen Analysis (PCA) to classify meat sample data so that it can be known which samples are pure beef, chicken, pork and beef, chicken mixed with pork.

Experiments

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Tools and Materials

The original version of this paper is available on https://www.scientific.net/EI.15.9.pdf

The ingredients used in this study were beef top side, fresh pork top side, and chicken breast. All fresh meat used in this study was obtained from Hokky Market in Surabaya.

Supporting tools used include: portable spectrophotometer with AS7341 sensor, knife, teleman, anallitic balance scale, coper cosmos CB 802 blender, gloves, tissue.

Design of Portable VIS-NIR Spectrophotometer with AS7341 Sensor

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The main components used to make the spectrophotometer are AS7341 sensor, raspberry pi 4B as a microcontroller, 5 inch TFT hdmi lcd, jumper cable, hdmi usb cable, tab usb cable, and laptop. Figure 1 shows the AS7341 VIS-NIR sensor which consists of 11 channels: 8 visible channels, 1 NIR channel, 1 non-filtered channel, and 1 flicker detection channel [35-36]. It is used for color analysis and spectral analysis with spectral response determined in wavelengths of 410 to 940 nm [26,37,38]. Figure 2 shows the schematic circuit of the VIS-NIR spectrophotometer.

Fig. 1. (a) The VIS-NIR spektrofotometer AS7341: breakout board and block diagram, (b) Layout of sensor array, (c) AS7341 typical characteristic (normalized intensity) [39].

Fig. 1. (a) The VIS-NIR spektrofotometer AS7341: breakout board and block diagram, (b) Layout of sensor array, (c) AS7341 typical characteristic (normalized intensity) [39].

Fig. 2. Schematic of spectrophotometer with AS7341 sensor.

Fig. 2. Schematic of spectrophotometer with AS7341 sensor.

Figure 3 shows how to calibrate the AS7341 sensor [40].

Fig. 3. AS7341 Sensor calibration.

Fig. 3. AS7341 Sensor calibration.

Tristumulus spectral power distribution (xyz):

With the conversion to tristumulus color coordinates (xyz), the color of light can be expressed in terms of two chromaticity coordinate indices:

Data Collection Procedure

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Figure 4 shows the step-by-step meat testing process: Whole meat ingredients that have been cleaned are cut into small pieces and then mashed by chopping and grinding one by one alternately using a blender for 10 minutes. The materials used were measured using analytical balance scales with the same weight, namely 10 grams of beef, 10 grams of pork, 10 grams of chicken, a mixture of beef and pork with concentrations ( ), and a mixture of chicken and pork with concentrations (7:3, 5:5, 1:9). Materials that were ready to be tested were placed on a cup. The spectrophotometer was turned on for 5 minutes before taking measurements. The material was then brought closer to the AS7341 sensor with a distance of 3 cm and ready for spectra detection.

Figure 4 shows how the developed tool works, namely the flash led sensor will light up on the meat sample and be reflected and received back by the photodiode. The reflected light will be received by the photodiode as a wavelength that represents a certain color spectrum. The color spectrum is converted by the photodiode into an analog signal and forwarded to the ADC into a digital signal. The digital signal is then processed by the raspberry pi 4B through various programming procedures which are finally translated into measurement results in the form of numbers in the form of wavelengths. The numbers of measurement results are displayed on the LCD and recorded and stored in csv format. The meat spectra measurement process is carried out for 3 minutes.

Fig. 4. Meat testing process.

Fig. 4. Meat testing process.

Data Analysis with Principal Componen Analysis (PCA)

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Data analysis was carried out on the spectra measurement results of each meat. Measurement data stored in CSV form is analyzed using the PCA method by looking at the results of reduction and projection through PC1 and PC2 regions that are able to explain the percentage of classification of each beef, pork, chicken, mixed beef-pork, and mixed chicken-pork.

Result and Discussion

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Test Response of AS7341 Sensor to Beef-Pork and Mixed Beef-Pork Samples

The original version of this paper is available on https://www.scientific.net/EI.15.9.pdf

Figure 5 and Figure 6 show the sensor response graphs for pure beef, pure pork, and mixed beef-pork samples, respectively.

Fig. 5. AS7341 Sensor response chart to meat samples (a) beef, (b) pork.

Fig. 5. AS7341 Sensor response chart to meat samples (a) beef, (b) pork.

Fig. 6. Response graph of AS7341 sensor to samples of (a) 7:3 beef-pork mixed meat, (b) 5:5 beef-pork mixed meat, (c) 1:9 beef-pork mixed meat.

Fig. 6. Response graph of AS7341 sensor to samples of (a) 7:3 beef-pork mixed meat, (b) 5:5 beef-pork mixed meat, (c) 1:9 beef-pork mixed meat.

Test Response of AS7341 Sensor to Chicken, Pork and Mixed Chicken-Pork Meat Samples

The original version of this paper is available on https://www.scientific.net/EI.15.9.pdf

Figure 7 and Figure 8 show the sensor response graphs for pure chicken, pure pork, and chickenpig mixed meat samples, respectively.

Fig. 7. AS7341 Sensor response chart to meat samples (a) chicken, (b) pork.

Fig. 7. AS7341 Sensor response chart to meat samples (a) chicken, (b) pork.

Fig. 8. Response graph of AS7341 sensor to samples of (a) 7:3 chicken-pork mixed meat, (b) 5:5 chicken-pork mixed meat, (c) 1:9 chicken-pork mixed meat.

Fig. 8. Response graph of AS7341 sensor to samples of (a) 7:3 chicken-pork mixed meat, (b) 5:5 chicken-pork mixed meat, (c) 1:9 chicken-pork mixed meat.

Figure 5 and Figure 8 are the response results of a portable spectrophotometer using the AS7341 sensor capable of measuring the spectral response to light reflected on each meat surface so as to provide information about the intensity of light in each spectral channel F1, F2, F3, F4, F5, F6, F7, F8, and NIR measured by the sensor. In the sample testing results, it can be seen that the AS7341 sensor response produces different characteristics in each type of beef, pork, chicken, mixed beef-pork, and mixed chicken-pork samples.

The spectra characteristics of each meat are:
a) Figure 5a shows pure beef has the highest to lowest intensity values in spectral channels: F7, F6, F8, F5, F4, F2, F3, NIR and F1.
b) Figure 5b shows pure pork has the highest to lowest intensity values on spectral channels: F7, F6, F5, F4, F8, F2, F3, NIR, F1.
c) Figure 6a and Figure 6b show mixed beef-pork (7:3), (5:5) has the highest to lowest intensity values in the spectral channels: F7, F6, F8, F5, F4, F2, F3, NIR, F1
d) Figure 6c shows the beef-pork mix ( ) has the highest to lowest intensity values in the spectral channels: F7, F6, F5, F8, F4, F2, F3, NIR, F1.
e) Figure 7a shows chicken meat has the highest to lowest intensity values in the spectral channels: F6, F5, F7, F4, F8, F2, F3, NIR, F1.
f) Figure 7b shows pure pork has the highest to lowest intensity values on spectral channels: F7, F6, F5, F4, F8, F2, F3, NIR, F1.
g) Figure 81, 8b, 8c shows chicken-pork mixed meat (7:3), (5:5), (1:9) has the highest to lowest intensity values in the spectral channels: F6, F7, F5, F4, F8, F2, F3, NIR, F1.
The difference between pure beef, pure pork, pure chicken, mixed beef-pork, and mixed chicken-pork lies in the spectral channels: pure beef: F8, F5, F4, pure pork: F5, F4, F8, pure chicken: F6, F5, F7, mixed beef-pork (7:3), (5:5): F6, F8, F5, beef-pork mix (1:9): F6, F5, F8, chicken-pig mix (7:3), (5:5), (1:9): F6, F7, F5.

The difference in spectral values in each meat is influenced by myoglobin which is the main pigment that gives color to meat. Myoglobin consists of: oxymyoglobin ( OxyMb ) which is cherry red in fresh meat, deoxymyoglobin (DeoxyMb) which is purplish red in vacuum-packed meat, and metmyoglobin (MetMb) which is brownish red resulting from oxidation making the meat pale or not fresh [41-43]. In addition, meat is a translucent material. When light hits the surface of the meat, the light is not only absorbed by myoglobin but some is reflected to the surface to produce a red color in the meat and some also penetrates the surface and interacts with the microstructure below the surface so that in this case absorption affects the color and scattering affects the brightness or darkness of the meat [44-45]. Subsurface scattering depends on the wavelength of the incident light, the size of the scattering particles, and the directional inhomogeneity of the tissue [44].

It can be seen that the portable spectrophotometer using the AS7341 sensor is able to produce different pattern characteristics for pure meat samples and mixed meat samples, thus further analysis can be carried out to obtain more informative results for sample classification analysis.

Classification Analysis of Pure Meat and Mixed Meat with Principal Componen Analysis (PCA)

The original version of this paper is available on https://www.scientific.net/EI.15.9.pdf

Figure 9 and Figure 10 show the results of PCA analysis of each meat. PCA analysis in this study was conducted to determine differences in the distribution of spectra data from beef, pork, chicken, mixed beef-pork, and mixed chicken-pork.

Fig. 9. Score plot of PCA spectra of beef, pork and beef-pork mixture.

Fig. 9. Score plot of PCA spectra of beef, pork and beef-pork mixture.

Fig. 10. Score plot of PCA spectra of chicken, pork and chicken-pork mixture.

Fig. 10. Score plot of PCA spectra of chicken, pork and chicken-pork mixture.

Figure 9 shows the results of the PC1 percentage of and PC2 of , indicating that the points (clusters) of beef are separated from the points (clusters) of pork. Meanwhile, the mixed beef-pork meat gathers and overlaps with pork. Figure 10 shows the results of PC1 by and PC2 by , indicating that the points (clusters) of chicken and pork spectra are separated while the chicken-pork mixture gathers with pork. This explains that PCA can classify or distinguish between pure beef, pure pork, pure chicken and mixed meat, namely beef-pork, chicken-pork.

The limitation of this research is that until now it has only considered the value of the spectral response to light reflected on each meat surface. It is more interesting for further research to also add meat aroma detection with a gas sensor and meat color with a color sensor. Further research can also be carried out using more sample variations to determine the wider potential of the AS7341 spectrophotometer. Data analysis and comparison with several other methods can also be carried out to find out which method is the most effective.

Summary

The original version of this paper is available on https://www.scientific.net/EI.15.9.pdf

In general, a Portable VIS-NIR spectrometer with AS7341 sensor combined with Principle Componen Analysis (PCA) has been successfully made to detect adulteration of beef, chicken meat mixed with pork. The response results from the sensor can identify differences in spectra on each meat shown in the PCA score plot on beef, pork and mixed beef-pork PC1 percentage of and PC2 of while in chicken, pork and mixed chicken-pork PC1 percentage of and PC2 of .

Acknowledgment

The original version of this paper is available on https://www.scientific.net/EI.15.9.pdf

The authors would like to thank DRPM ITS for funding this research through a scientific research scheme sourced from ITS funds in 2023 with TM/DRPM-ITS/PN.02.003.

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The original version of this paper is available on https://www.scientific.net/EI.15.9.pdf

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The original version of this eBook is available on https://www.scientific.net/book/engineering-innovations-vol-15/978-3-0364-1968-8

Engineering Innovations, ISSN: 2813-1002, Vol. 15, pp 21-32

doi: 10.4028/p-O9sSAg

© 2025 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2025-03-12

Revised:
2025-07-08

Accepted:
2025-07-09

Online:
2025-11-18

Fault Detection in Spur Gears Through Vibration Signal Analysis and Machine Learning Techniques

The original version of this paper is available on https://www.scientific.net/EI.15.21.pdf

Muhammad Talha Asif , Shahzad Ahmad , Asif Khan , Sohail Malik

Faculty of Mechanical Engineering, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Topi-23640, Swabi, Khyber Pakhtunkhwa, Pakistan

Faculty of Mechanical Engineering, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Topi-23640, Swabi, Khyber Pakhtunkhwa, Pakistan

Mechanical Engineering Department, Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia

Mechanical Manufacturing and Automation, Pak-Austria Fachhochschule Institute of Applied Sciences and Technology, Mang Haripur, Pakistan

sohail.malik@fcm3.paf-iast.edu.pk

Keywords: Short-Time Fourier Transform, Scalograms, Machine Learning, Fault Diagnosis, Gears Condition Monitoring.

Abstract

This research uses a multi-domain technique to give a thorough analysis of mechanical gears health evaluation that includes time, frequency, and time-frequency signal analysis. The research seeks to discover patterns indicative of healthy, partially damaged, or fully damaged gear states using a variety of graphical representations, including time and frequency plots, the Short-Time Fourier Transform (STFT), and scalograms which are visual representations of the wavelet transform of a signal. Advanced machine learning models are used to improve diagnostic accuracy when manual identification of these trends becomes difficult. The goal is to achieve a validation accuracy greater than a threshold selected based on prior studies indicating that this level ensures reliable fault detection for industrial applications while balancing computational constraints. The reliability and effectiveness of gear monitoring systems can be increased by integrating contemporary signal processing and machine learning approaches, as demonstrated by this research, which also advances the identification of gear faults. Among the conclusions are the outcomes of tests done to identify gear problems in which authors were able to train a model with more than accuracy and able to propose Artificial Intelligence model for classification of faults in gears.

1. Introduction

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Mechanical gears are a fundamental part in many industrial applications such as automotive, aerospace, and manufacturing systems [1]. Failure to this part can lead to costly downtime, operational inefficiencies, and potential safety hazards [2]. So, to avoid these unexpected failures, the condition monitoring of gears has become a critical area of research in which vibration signal analysis is widely recognized for its ability to detect anomalies in gear operation [3]. Traditional fault detection approaches, such as time-domain and frequency-domain analyses struggle to identify early-stage defects due to the complex nature of vibration signals [4]. While techniques like Fast Fourier Transform (FFT) and Short-Time Fourier Transform (STFT) provide frequency-based insights and may fail to capture non-stationary signal variations. So, as a result, machine learning (ML) techniques have emerged as a powerful alternative, enabling automated fault detection by recognizing patterns that are not easily distinguishable through manual inspection [5]. A time frequency analysis was applied in industrial bearing and a pattern was observed for fault with a specific behavior but lacks accuracy at specific instants [6]. Vibration signals and thermal images were used for CNN, a type of deep learning model, but the model failed at some instant when there are uneven surrounding interventions [7]. At different variable working conditions signal processing, machine learning techniques and deep learning models were implemented, but the model needs high computational
power and difficult to implement at industrial sites [8]. Combination of 1D and 2D CNN used in Power Quality Disturbance (PQD) classification but requires processing time for training of neurons [9]. Hybrid Genetic Feature Selection (HGFS) framework was developed but lacks computational efficiency and requires a lot of processing time [10]. Recent studies, such as [9] have explored the use of deep reinforcement learning for real-time gear fault diagnosis, achieving higher accuracy but still facing challenges in computational efficiency. A web application that takes machine sensor data as input to predict the possibility of downtime of the machine and it needs good accuracy of the model to predict the life of the gears [11]. Based on previous literature, the current trends have a limitation of computational storage as with the adopted implementation of deep leaning and feature extraction it requires a much computational power in the form of Graphical Processing Unit [12]. Also, it will not be feasible for real-time monitoring. So based on these, the simplified approach needs to be adopted in which a machine learning-based fault diagnosis framework needs to implement that integrates signal processing and supervised learning for enhancing the accuracy of gear condition monitoring [13]. Different feature extraction and selection techniques need to be implemented to optimize the dataset and train a classification model to distinguish between different conditions in gear and predict their health through predictive maintenance [14].
Based on the previous work it is desired to do fault diagnosis of gears through a simplified approach which can also be implemented in Internet of Things. Therefore, in this research, a setup is fabricated in which meshing of gears performed and data is extracted through sensor and different signal processing techniques were implemented in it. Finally, supervised learning is performed though which AI model obtained and implemented for real-time diagnosis of gears.

2. Methodology

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The overall methodology includes a data acquisition process for the meshing of gears section. Arduino Mega 2560 Rev3 acquired that data and then processed further in MATLAB for fault detection in gears as shown in Figure 1.

Fig. 1. Workflow of Experiment

Fig. 1. Workflow of Experiment

2.1. Experimental Setup

The experimental setup used in this study is a gear-driven mechanical system with two shafts in which one shaft is attached to a DC motor directly while the other shaft is free at both ends. Different voltages were applied to the D.C motor and meshing of the gears were performed for different conditions of the spur gears. Initially three different classifications were implemented in the gears. Containing Healthy Gears in which the spur gears are in their healthy state, Partial Broken Teeth in which one of the teeth is broken and Partially Broken Gear in which a complete tooth is broken as shown in Figure 2.

Fig. 2. a. Healthy(H) b. Partially Broken Gear (PBG) c. Partially Crack Teeth (PCT)

Fig. 2. a. Healthy(H) b. Partially Broken Gear (PBG) c. Partially Crack Teeth (PCT)

The experimental setup is such that gears are attached to the shafts aligned parallel to each other and rotation of one shaft causes the meshing of the two spur gears. The shafts are passed through bearing housing in which an accelerometer is mounted that is responsible for capturing vibration of gears. The data is captured on Arduino Mega 2560 Rev3 and further processed in SUMILINK. The Arduino Mega 2560 Rev3 was selected for its high analog input resolution (10-bit ADC), sufficient digital I/O pins (54), and compatibility with Simulink for real-time data acquisition, making it more suitable than alternatives like Arduino Uno or Raspberry Pi [15]. The experimental setup is shown in Figure 3.

Fig. 3. a. Experimental Setup b. Accelerometer Axis

Fig. 3. a. Experimental Setup b. Accelerometer Axis

The accelerometer (ADXL335) is fixed to the bearing housing and records vibration on two axis that is x -axis and z -axis along the third axis where vibration is zero as it is along the shaft, so there will be minimum vibration in that axis. The data is acquired from only x -axis and z -axis to record maximum vibrations of the bearing housing generated through the gear meshing.

2.2 Data Acquisition

To gather and analyze data in real-time for this investigation, the Arduino Mega 2560 Rev3 microcontroller was integrated with the ADXL335 accelerometer and Simulink. The sampling time was adjusted to remove aliasing errors in it and for its Nyquist frequency criteria is implemented to set the sampling rate equal to twice the operating frequency [16]. The data is acquired at three distinct voltage levels that are at , and 20 V . These voltages chosen to simulate a range of operational
speeds typical in industrial gear systems, capturing diverse dynamic behaviors [17]. The Simulink model ran for 10 seconds for each voltage level, recording 1001 data points for the vibrations of the X -axis and Z -axis. The dataset was divided into for training, for validation, and for testing, as this arrangement offered enough resolution to record the mechanical vibrations produced by the shafts and gears under various operating circumstances. Signal processing and fault categorization were then performed on the data [18].

2.3. Signal Processing

To examine and contrast the behavior of the gear system under the three operating conditions the healthy (H), partially broken gear (PBG), and partially cracked teeth (PCT) a variety of signal processing techniques were applied to the vibration data to find any noticeable variations in the vibration patterns that would lead to gear failures. The signal processing techniques used in this research are time-domain analysis, frequency-domain analysis with the Fast Fourier Transform (FFT), and time-frequency analysis with the Short-Time Fourier Transform (STFT).

2.3.1. Time-Domain

Time-domain analysis was used to track variations in vibration amplitude over time and look for any obvious anomalies like spikes or irregularities that would point to gear failures. The vibration data time-domain signals were made for each of the three gear circumstances at each voltage levels as shown in Figure 4. At the time-domain approach there were no appreciable visual variations between the PCT, PBG, and Healthy sections and no anomaly in the data from healthy and defective gears was identified. This is due to the noise factor that the sensor encounters during vibration measurements.

Fig. 4. Time-Domain Vibration Signals for Healthy, Partially Broken Gear, and Partially Cracked Teeth Conditions at , and 20 V

Fig. 4. Time-Domain Vibration Signals for Healthy, Partially Broken Gear, and Partially Cracked Teeth Conditions at , and 20 V

2.3.2. Frequency domain analysis

The Fast Fourier Transform (FFT) was performed on time-domain data to obtain additional understanding of the frequency properties of the vibration signals. By breaking down the timedomain signal into its frequency components, this transformation makes it possible to pinpoint prominent frequencies that may be connected to gear failures. For each of the three voltage levels the FFT was figured out and the frequency spectrum that was produced were shown for comparison as shown in Figure 5.

Fig. 5. Frequency-Domain Spectra of Vibration Signals for Healthy, Partially Broken Gear, and Partially Cracked Teeth Conditions at , and 20 V

Fig. 5. Frequency-Domain Spectra of Vibration Signals for Healthy, Partially Broken Gear, and Partially Cracked Teeth Conditions at , and 20 V

As shown in Fig.6, same as for time-domain analysis, the frequency spectra did not show any significant differences between Healthy, PBG, and PCT conditions, which means a more advanced approach is required for accurate prediction of gears.

2.3.3. Fourier Transform in Short Time (STFT)

STFT was used to capture the time-varying nature of the frequency content in the vibration signals. It is possible to observe how the frequency components vary over time through STFT time frequency representation of the signal. Therefore, the behavior of signal in frequency and time domain can be checked. This is especially helpful in identifying intermittent defects or transient behavior that might not be seen in the whole frequency band. As shown in Figure 6 STFT results did not show any noticeable or consistent differences between the working and malfunctioning gears despite this thorough investigation and further confirming the subtlety of the faults.

Fig. 6. STFT at each signal

Fig. 6. STFT at each signal

2.3.4. Scalograms

The Scalograms, which are visual representations of the wavelet transform of a signal, are used to analyze time-frequency characteristics. They are generated when there is a need for time localization for short duration and high frequency events. The scalogram images are produced through applying wavelet transform to the signal. The scalograms of all the profiles are shown in Figure 7. In the current
case, it also does not show any prominent pattern which can be used as an identification to draw some useful conclusion regarding the profile of gear.

Fig. 7. Scalogram Images

Fig. 7. Scalogram Images

The scalograms analytical methods showing the vibration signals from the healthy, partial broken gear, and partial crack teeth gears did not show any appreciable or distinct changes. The discovery implies that conventional signal processing techniques would not be enough in capturing the nuanced aspects of gear defects in this specific configuration. Consequently, machine learning techniques will be further investigated for improved fault identification.

3. Machine Learning Model Development

The original version of this paper is available on https://www.scientific.net/EI.15.21.pdf

The model was trained through supervised learning. The steps involved are divided into the preparation of data and through supervised learning training of developed model through labeled data as shown in Figure 8. The cross validations of 10 are adopted in which the data is divided into 10 sections (folds) that means that on nine sections, the model is trained, and on the remaining part, it is tested [19]. Ten times through this process, one test set is used for each fold. By using this method, overfitting is prevented, a situation in which the model performs well with training data but badly with unknown data.

Fig. 8. ML Model workflow

Fig. 8. ML Model workflow

3.1. Feature Extraction

A total of 15 features were extracted from the extracted signals that belongs to time-domain and frequency-domain. The time-domain features are calculated using statistical measures from the raw signal over time. It is used to capture useful characteristics of amplitude variations over time and are used in various domains, especially in fault diagnosis. There are a total of 15 features in time-domain and in spectral kurtosis shown in Table 1.

Table 1. Time Domain Features

Sr# | Features
1 | Mean
2 | Standard Deviation (Std)
3 | Skewness
4 | Kurtosis
5 | Peak-to-Peak
6 | Root Mean Square (RMS)
7 | Crest Factor
8 | Shape Factor
9 | Impulse Factor
10 | Margin Factor
11 | Energy

Table 2. Spectral Kurtosis Features

Sr# | Features
1 | Spectral Kurtosis Mean
2 | Spectral Kurtosis Std
3 | Spectral Kurtosis Skewness
4 | Spectral Kurtosis Kurtosis

3.2 Feature Selection

There was a total of 15 features that were extracted shown in Table 1 so RelieF algorithm used which is a feature selection method that ranks features based on their relevance to the target variable to identify and remove features with a negative impact that are identified and removed. So, after using RelieF algorithm the results are shown in Figure 9.

Fig. 9. Selected Features Using RelieF algorithm

Fig. 9. Selected Features Using RelieF algorithm

The results illustrate the feature selection process using the RelieF algorithm, which evaluates all 15 features shown in Table 1 and removes four with negative impact, resulting in an optimized set of 11 features for model training. Consequently, a total of 11 features were extracted and the model is trained based on these features to enhance the accuracy.

3.3. Results and Discussions

The model shows accuracy through the Fine Tree Model. The accuracy of all the trained models is shown in Figure 10.

Fig. 10. ML Model Comparison

Fig. 10. ML Model Comparison

Through it can be identified, the accuracy of Fine Tree and Medium Tree AI models gives the highest accuracy, generating overall accuracy of . The confusion matrix is shown in Figure 11.

Fig. 11. Confusion matrix

Fig. 11. Confusion matrix

The trained model can predict the Healthy state with an accuracy of and have a high ability to predict the defective two cases to be . The Receiver Operating Characteristic (ROC) curve is plotted in which the relation is between the true positive rate (sensitivity) versus the false positive rate for various categorization thresholds. It gives an indication of how well the model can distinguish between the various classes. Area Under the ROC Curve (AUC) summarizes the model ability to distinguish between positive and negative classes in a single numerical value. Excellent performance is indicated by a model with an AUC near 1, whereas performance that is no better than random guessing is represented by an AUC of 0.5. The ROC curve of trained model is shown in Figure 12.

Fig. 12. ROC Curve

Fig. 12. ROC Curve

It shows that the AUC to be closed to one for the defective two cases and around 0.81 for the Healthy state. The above results show the accuracy of the model which can be implemented to any system to figure out the health assessment of spur gears and in the later stage able to predict the maintenance of spur gears through statistical analysis of the required-results. The result produced showed the Tree model to have most accuracy compared to all the other models.

Model 2.1: Tree
Status: Trained
Training Results
Accuracy (Validation) 72.2%
Total cost (Validation) 50
Prediction speed ~1000 obs/sec
Training time 17.172 sec
Model size (Compact) ~13 kB

Fig. 13. Trained Model Result.

The Tree model was successfully trained and validated giving validation accuracy of showing that nearly this percent of prediction made by model validation were correct. The total validation cost is shown in Figure 13 in which the total cost value of 50 refers to the misclassification cost or loss function value during validation. It basically indicates a predefined penalty on the model performance during validation. Also, the model has the capability of predicting 1000 observations per second. The approximately time of training is 17.172 seconds showing that it is trained relatively faster. The model's weight is approximately 13 kB , showing a lightweight mode.
The validation accuracy of achieved by the Fine Tree Model exceeds the target of , which was selected as a practical benchmark based on the complexity of the vibration signals and the computational limitations of real-time monitoring systems. Achieving higher accuracy, such as , would require more advanced feature extraction techniques or deeper models, which could increase computational demands and make the system less feasible for industrial deployment. Conversely, an accuracy below would likely result in unreliable fault detection, as observed in prior studies [20], leading to increased false positives or negatives. So, based on this, the overall performance is good, suitable for real-time monitoring and useful where computational efficiency is of priority.

4. Conclusion

The original version of this paper is available on https://www.scientific.net/EI.15.21.pdf

The main objective of this study was to use an accelerometer to collect data from the gear meshing profile, which was well accomplished. Many signals processing methods, including time-domain analysis, frequency-domain analysis, Short-Time Fourier Transform (STFT), and scalograms, were applied to the raw vibration data. Even though, the signal processing techniques were unable to produce patterns that might be used to discern between the various states of the equipment. This made a more sophisticated method necessary. These limits were addressed using machine learning, which developed a supervised learning model with an initial accuracy of by extracting 15 important features from the raw signal. Eleven optimized features remained after more refining using the RelieF algorithm eliminated any characteristics that had a detrimental effect or were superfluous. As a result, the accuracy of the model increased to . The Tree model was proposed giving the highest accuracy compared to others adopting the current scheme of work. The model presented in this work is a potential real-time gear condition monitoring solution, and its application to Industry 4.0 contexts for predictive maintenance could be expanded. Additionally, this work shows that feature refinement can improve the accuracy of machine learning-based models. Subsequent investigations may investigate the utilization of deep learning methodologies, such as those proposed by[21], to enhance the precision and versatility of the model on increasingly intricate datasets. While the model achieved a validation accuracy of , surpassing the target, future recommendations could explore advanced deep learning techniques to push the accuracy closer to , potentially at the cost of increased computational resources.

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The original version of this paper is available on https://www.scientific.net/EI.15.21.pdf

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This ePub file was created using machine-generated methods and automated software to convert the source text into an ePub format and may contain formatting inconsistencies from the original version. Formatting may vary depending on the device used.

The original version of this eBook is available on https://www.scientific.net/book/engineering-innovations-vol-15/978-3-0364-1968-8

Engineering Innovations, ISSN: 2813-1002, Vol. 15, pp 33-42

doi: 10.4028/p-F92q4U

© 2025 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2025-06-06

Revised:
2025-06-14

Accepted:
2025-06-18

Online:
2025-11-18

Development of an loT-Enabled Smart Home Automation System

The original version of this paper is available on https://www.scientific.net/EI.15.33.pdf

M. N. Afnan Uda , Uda Hashim , M. N. A Uda , Ahmad Haziq Zulkifli , Widyastuti , Retno Asih , Yuri Pamungkas , N.H. Ibrahim

Faculty of Engineering, Universiti Malaysia Sabah (UMS), 88400 Kota Kinabalu, Sabah, Malaysia

Faculty of Mechanical Engineering & Technology, Universiti Malaysia Perlis (UniMAP), 02600 Arau, Perlis, Malaysia

Institute of Nano Electronic Engineering, Universiti Malaysia Perlis (UniMAP), 01000 Kangar, Perlis, Malaysia

Department of Material and Metallurgical Engineering, Institut Teknologi Sepuluh Nopember (ITS), 60111 Surabaya, Indonesia

Department of Physics, Institut Teknologi Sepuluh Nopember (ITS), 60111 Surabaya, Indonesia

Department of Medical Technology, Institut Teknologi Sepuluh Nopember (ITS), 60111 Surabaya, Indonesia

Faculty of Chemical Engineering & Technology, Universiti Malaysia Perlis (UniMAP), 02600 Arau, Perlis, Malaysia

nurafnan@ums.edu.my, uda@ums.edu.my , nuraiman@unimap.edu.my, haziqajik010602@gmail.com, wiwid@its.ac.id, retno.asih@its.ac.id, yuri@its.ac.id, hhulwaniibrahim@gmail.com

Keywords: Energy Efficiency, ESP8266, Home Automation, Internet of Things, Mobile Application, Sensors

Abstract

The integration of Internet of Things (IoT) technologies is transforming homes by improving energy efficiency, safety, convenience, and even health. This paper presents the development of an IoT-enabled smart home automation system designed to enhance these aspects using cost-effective components and user-friendly control methods. The system utilizes the ESP8266 microcontroller along with KY-018 Light and HC-SR04 Ultrasonic Sensors to collect environmental data. Core features include real-time visual feedback through LEDs and LCD I2C displays, and remote operation via a custom Android mobile application integrated through Firebase. Both automated and manual control modes are provided, ensuring seamless and adaptive responses to realworld conditions. Experimental results confirm accurate detection and reliable control actions, demonstrating the practicality and scalability of this smart home automation system.

Introduction

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The rapid advancement of technology has enabled the evolution of smart homes, greatly enhancing daily living through improved convenience, security, and energy efficiency [1]-[3]. Beyond these core benefits, smart homes also play a vital role in fostering healthier indoor environments by regulating lighting and air circulation, minimizing manual tasks, and supporting elderly and healthrelated care through automation. This study focuses on the development of an IoT-based smart home automation system powered by the ESP8266 microcontroller [4]-[7]. The system incorporates KY018 Light Sensors and HC-SR04 Ultrasonic Sensors to monitor environmental parameters critical to home automation. These sensors work in tandem with LEDs and an LCD I2C display to deliver immediate visual feedback and system responsiveness [8]-[10].
Smart home automation has become increasingly important in tackling global challenges such as energy conservation, climate resilience, and technological accessibility [11]-[13]. Current systems often rely on advanced computing techniques like big data analytics, cloud services, and user-centered design to manage and optimize home environments [14]-[16]. Despite these advancements, the
widespread adoption of smart home technologies remains hindered by various barriers including technical complexity, high costs, unreliable internet access, and unfriendly user interfaces [17]-[19]. Addressing these challenges is crucial to improving the usability, affordability, and trust in smart home systems [20]-[22].
This research aims to develop a dual-mode IoT-enabled smart home system that integrates both automated and manual control mechanisms. The automated mode uses environmental inputs from sensors to trigger actions, while the manual mode allows users to operate devices remotely via a custom-built mobile application. The project involves the design and implementation of core hardware components, including the ESP8266 microcontroller, KY-018 Light Sensor, HC-SR04 Ultrasonic Sensor, LEDs, and an LCD I2C display. The effectiveness of the system is evaluated in terms of sensor performance, algorithm responsiveness, and user interface reliability. Overall, this work demonstrates the potential of IoT to deliver a practical, adaptable, and health-conscious smart home experience.

Methodology

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System Design

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This project adopts a dual-component methodology, comprising an Automatic Detection System and a Manually Controlled System, to create a comprehensive smart home automation solution. The Automatic Detection System utilizes sensors to enable hands-free, real-time management of home devices based on environmental data, automating tasks such as lighting, security, and energy regulation. In parallel, the Manually Controlled System empowers users with direct control via a custom mobile application connected through Wi-Fi, allowing for customization and manual overrides of automated settings. By integrating these two systems, the project achieves a balance between automation and user-driven control, enhancing safety, efficiency, and user convenience. This dual approach ensures a versatile and adaptive smart home system, contributing to an improved quality of life.

Circuit Diagram and Implementation

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The Automatic Detection System leverages an ultrasonic sensor to measure the proximity of objects by emitting ultrasonic sound waves and calculating the time it takes for the waves to reflect off an object. This process determines the distance to the object and triggers specific automated responses. For instance, if the sensor detects an object within a predefined range 0 to 5 centimeters, it activates an LED light, enhancing visibility and security. Additionally, a light sensor is integrated into the system to complement the ultrasonic sensor by detecting ambient light levels and adjusting appliance settings accordingly. For example, under low-light conditions, the light sensor can trigger the activation of lights, ensuring seamless and efficient automation that adapts dynamically to environmental conditions. Figure 1 illustrates the schematic diagram of the Automatic Detection System, while Figure 2 depicts its circuit implementation on a breadboard.

Fig. 1. Schematic Diagram for Automatic Detection System

Fig. 1. Schematic Diagram for Automatic Detection System

Fig. 2. Circuit Implementation on Breadboard

Fig. 2. Circuit Implementation on Breadboard

The Manually Controlled System, on the other hand, relies on a mobile application for managing the activation and deactivation of household devices. Through a successful Wi-Fi connection and user authentication, the application interfaces with the smart home system via the Firebase Realtime Database. This enables users to remotely control devices such as lights and appliances, providing flexibility and convenience. Designed for ease of use, the mobile application ensures that even users with minimal technical expertise can efficiently navigate its features to manage their home environment from anywhere with an internet connection.

Result and Discussion

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Hardware Analysis

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The automatic detection system plays a vital role in the IoT-powered smart home automation project, with the HC-SR04 ultrasonic sensor as a key component. This sensor measures the distance between itself and an object using the following Eq. (1).

Where D represents the distance, T is the time taken for the ultrasonic waves to reflect, and C is the speed of sound. Equation 1 forms the basis for calculating distances with reasonable accuracy in the system.

Fig. 3. Object Detection Distance Measurement

Fig. 3. Object Detection Distance Measurement

The sensor was tested to detect objects at various specific distances: cm , and 2 cm . As shown in Figure 3, when an object falls within the range of 0 to 5 cm , the LED light is activated. If the object is beyond this range, the LED remains off. The hardware analysis focused primarily on evaluating the accuracy of the ultrasonic sensor. In this test setup, objects were placed at different distances, and the sensor's readings were recorded. These values were then compared against expected measurements to assess any deviation or error. For example, at a target distance of 10 cm , the average measured value was 10.0657 cm , yielding an error percentage of , demonstrating the sensor's precision.
The integration of the light sensor further enhances the overall system's functionality. During nighttime, the system automatically activates outdoor lamps, and in the morning, it deactivates them. This automatic adjustment not only increases convenience but also contributes to energy efficiency by ensuring that lights are only used when necessary.

Software Analysis

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The manually controlled system enables users to manage the smart home automation system through a dedicated mobile application. Developed using Android Studio, the app interfaces with the ESP8266 microcontroller via Firebase Realtime Database, a cloud-based platform designed for real-time data synchronization. The mobile app incorporates an intuitive user interface to ensure ease of use for controlling household appliances remotely. Upon establishing a successful Wi-Fi connection, the app authenticates users through Firebase. Once authenticated, users can toggle devices on or off directly from the app, as illustrated in Figure 4. For example, when a user presses a button in the app, Firebase updates its database in real-time, sending a signal to the ESP8266 microcontroller to control the corresponding hardware, such as an LED light.

Fig. 4. Mobile Application

Fig. 4. Mobile Application

This process guarantees real-time communication and precise control. For instance, if a user activates the living room LED via the app, Firebase immediately registers this input, and the ESP8266 microcontroller processes the change to illuminate the LED. This integration demonstrates the system's responsiveness and effectiveness. Table 1 shows the summary of system responses for different input conditions, illustrating how the sensors and control methods affect the automation behavior.

Table 1. Summary of Input Conditions and System Responses

Input Condition | Sensor or Trigger | System Response
Object detected (≤5 cm) | Ultrasonic Sensor | LED ON, LCD displays "WELCOME BACK"
No object detected (>5 cm) | Ultrasonic Sensor | LED OFF
Low ambient light | Light Sensor | Lamp ON
Bright ambient light | Light Sensor | Lamp OFF, LCD displays "Its Morning"
App button: ON | Firebase via Mobile App | Specific LED ON
App button: OFF | Firebase via Mobile App | Specific LED OFF

Complete Design Analysis

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Automatic Detection System

The original version of this paper is available on https://www.scientific.net/EI.15.33.pdf

The light sensor was evaluated to ensure proper functionality under different lighting conditions. Its primary role is to manage lighting based on ambient light levels, enhancing energy efficiency. In the absence of light, the yellow LED, functioning as an outdoor lamp, illuminates as shown in Figure 5. This simulates real-world scenarios where the outdoor lamp automatically turns on at night when the sensor detects darkness. Conversely, during daylight, the light sensor disables the lamp to conserve energy.

Fig. 5. No Light Detected

Fig. 5. No Light Detected

When ambient light is detected, as illustrated in Figure 6, the outdoor lamp LED turns off, and the LCD displays the message "Its Morning." This dynamic response mirrors practical applications, where the system adapts to varying environmental conditions, contributing to sustainability and user convenience.

Fig. 6. Light Detected

Fig. 6. Light Detected

The ultrasonic sensor was analyzed for its ability to detect objects within a specified proximity and automate responses accordingly. When the sensor identifies an object within a range of , it triggers the LCD to display the message "WELCOME BACK," and all LEDs light up, as shown in Figure 7 and Figure 8. In practical applications, the sensor can be calibrated for longer detection ranges, such as meters, to control household appliances like lights, fans, or air conditioners. This adaptability enhances proximity-based automation, improving the overall functionality and convenience of the smart home system. Furthermore, users can customize the detection range to suit their specific needs, thereby enhancing satisfaction and usability.

Fig.7. Object Detected

Fig.7. Object Detected

Fig. 8. LED On After Object Detected

Fig. 8. LED On After Object Detected

Manual Controlled System

The original version of this paper is available on https://www.scientific.net/EI.15.33.pdf

The Manual Controlled System for home automation relies on a mobile application developed with Android Studio to manage device outputs. The process begins by establishing a Wi-Fi connection, followed by user authentication via Firebase. Once authenticated, users can control various household devices, such as LEDs in different rooms, directly through the app. This setup allows for remote control of devices, providing a convenient and user-friendly experience. Firebase enables seamless communication between the mobile app and hardware components, ensuring real-time data synchronization and efficient control of the smart home environment.

Fig. 9. Light in All Room On

Fig. 9. Light in All Room On

Fig. 10. All Light Off

Fig. 10. All Light Off

Figures 9 and 10 illustrate the outputs of the smart home using the manual control method through the Android Studio-based application, with all outputs managed via this app interface. When the user presses a button for a specific room, such as Living Room, Kitchen, or Bedroom, the LED associated with that room turns on. The corresponding change is reflected in Firebase, where the ledStatus value is updated to 1 , and the LED is activated. Conversely, when the red color appears on the button, indicating the LED should be off, the ledStatus value in Firebase is set to 0 , and the LED turns off.

Conclusion

The original version of this paper is available on https://www.scientific.net/EI.15.33.pdf

This project successfully demonstrates the integration of various hardware components, including the ESP8266 microcontroller, light sensors, ultrasonic sensors, LEDs, and an LCD display, alongside a custom Android application for efficient smart home automation. The system facilitates remote control and real-time data synchronization, significantly enhancing user convenience and operational flexibility. The testing phase confirmed the system's reliability and performance under different conditions, highlighting its potential for practical application in smart homes. Future improvements may include the use of more advanced microcontrollers with higher processing power to accommodate additional sensors and actuators. Enhancing sensor accuracy and reliability, integrating machine learning algorithms for predictive automation, and ensuring a stable power supply are crucial areas for development. Additionally, integrating face recognition technology for personalized access control and CCTV cameras for real-time surveillance could significantly enhance both the security and functionality of the system.

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The original version of this paper is available on https://www.scientific.net/EI.15.33.pdf

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M. N. Afnan Uda et al., "Aluminium Interdigitated Electrode with Gap for Electrolytic Scooting," Int. J. Nanoelectron. Mater., vol. 17, no. Special issue, pp. 237-243, 2024, doi: 10.58915/ijneam.v17iJune.863.

M. N. Afnan Uda, A. B. Jambek, U. Hashim, and M. N. A. Uda, "Development of Voltage Amplifier Electronic Reader for Multiplex Detection of Two Electrode Electrical Biosensors," IOP Conf. Ser. Mater. Sci. Eng., vol. 743, no. 1, 2020, doi: 10.1088/1757-899X/743/1/012019.

M. Isa et al., "Arthropods-mediated Green Synthesis of Zinc Oxide Nanoparticles using Cellar Spider Extract: A Biocompatible Remediation for Environmental Approach," Int. J. Nanoelectron. Mater., vol. 17, no. Special Issue, pp. 211-219, 2024, doi: 10.58915/ijneam.v17iJune.860.

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I. Luiz, B. De Moura, L. Carlos, D. S. Monteiro, and F. A. Soares, "Low Cost Surface Electromyographic Signal Amplifier Based On Arduino Microcontroller," Int. J. Electr. Robot. Electron. Commun. Eng., vol. 8, no. 2, pp. 310-314, 2014.

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Engineering Innovations, ISSN: 2813-1002, Vol. 15, pp 43-52

doi: 10.4028/p-WdVi6D

© 2025 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2025-03-14

Revised:
2025-07-03

Accepted:
2025-07-03

Online:
2025-11-18

IoT-Based Smart Energy Management for Smart Grids Applied in a Home

The original version of this paper is available on https://www.scientific.net/EI.15.43.pdf

Abdullah Mushtaq , Wasim Ahmad

University of Engineering & Technology, Taxila, Pakistan

University of Engineering & Technology, Taxila, Pakistan

wasim.ahmad@uettaxila.edu.pk

Keywords: Smart Energy power, Energy Management, Smart Grids, IoT

Abstract

The growing demand for effective energy management in smart grids necessitates modern monitoring and optimization methods. Traditional energy distribution systems confront numerous issues, including high electricity costs, ineffective load management, and a lack of real-time data analytics. This study describes an IoT-based Smart Energy Monitoring System (SEMS) for continuous real-time energy tracking, with accurate current measurements provided via wireless transmission and predictive analytics. The system is designed to provide reliable and precise current measurements. The suggested system combines wireless data transmission and predictive analytics. The technology enables real-time energy monitoring and assists users in analysing consumption trends to make more informed decisions. It also allows for more efficient load control by identifying peak consumption periods. An energy audit validates the system, indicating considerable reductions in power losses as well as increased economic feasibility. The findings demonstrate that IoT-driven smart energy management improves sustainability, dependability, and cost effectiveness in modern smart grids.

Introduction

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Energy management and planning are becoming more complicated due to the growing demand for power worldwide and the incorporation of renewable energy sources. Issues with traditional power networks such as poor load distribution, energy waste, and a lack of real-time monitoring. Without effective energy management, utilities have trouble managing peak loads, and customers pay high power costs as a result of inefficient energy use [1]. Smart grid technology, which combines automation, real-time monitoring, and predictive analytics, has been introduced to address these issues. The use of IoT-based smart energy meters is one of the most promising strategies in smart grids. These gadgets give consumers comprehensive insights into their power usage, enhance load balancing, and allow real-time tracking of energy consumption. These smart meters increase energy efficiency by enabling remote monitoring and management through cloud connectivity [2]. Existing IoT-based energy monitoring systems have drawbacks despite their benefits. Large-scale implementation is challenging due to the high cost of many commercially available smart meters. Furthermore, certain technologies don't integrate well with the grid's current architecture, and connection problems prevent them from delivering reliable real-time data. Another issue is the dependence on proprietary software and hardware, which restricts flexibility and modification for various energy management requirements [3]. For effective load control and real-time energy tracking in smart grids, this article suggests a low-cost, Internet of Things-based smart energy monitoring system. With its wireless transmission and real-time data collecting capabilities, this Internet of Things-based energy meter guarantees precise energy usage monitoring. Users may optimize electricity use, cut down on waste, and help create a more sustainable and efficient energy infrastructure by connecting this gadget into smart grids.
The creation, deployment, and performance assessment of the suggested smart energy monitoring system are the main topics of this study, which emphasizes the advantages it offers to utility companies and customers alike [4, 5].

Methodology

The original version of this paper is available on https://www.scientific.net/EI.15.43.pdf

An IoT-based Smart Energy Management System (SEMS) suited for smart grid applications is designed, developed, and evaluated in this study using a systematic, multi-phase methodology. After a thorough analysis of current technologies, the system's architectural design, component integration, data collection, and performance assessment using simulation and real-world testing come next. [6,7].

Fig. 1. Energy audit flowchart showing the overall process.

Fig. 1. Energy audit flowchart showing the overall process.

The proposed framework (Fig. 1) is designed using a layered architecture, integrating IoT devices, edge computing nodes, block chain networks, and machine learning algorithms. This stage includes defining system components, communication protocols, and data flow mechanisms. Smart meters and IoT sensors are deployed to gather 24 -hour energy usage statistics, voltage fluctuations, and load profiles such as house hold or small-scale grid settings, to collect real-time energy consumption data, including usage patterns and load fluctuations. Data is processed locally to reduce delays and enable real-time analysis, with secure procedures in place to protect information and transactions. [8]. The planned IoT-based Smart Energy Management System (SEMS) was implemented and validated in residential structures with different structural configurations, categorized as single-, double-, and triple-unit homes. These residences varied in size from 05 to 20 Marla, and their floor patterns, occupant levels, and electrical load diversity became more complex. In contrast to double- and tripleunit residences, which house two or more households with separate consumption patterns and dispersed sub-loads, single-unit dwellings usually house one family and have a single load center. Different appliance densities, diversification variables, and usage simultaneity cause non-linear load characteristics in this variation. Consequently, the number of active households and the actual size of the residence were regarded as crucial factors for real-time energy profiling.Edge computing techniques pre-process the data, reducing latency and enhancing real-time decision-making. Advanced analytics, including predictive modelling, are applied to interpret the collected data, enabling forecasts of energy demand and optimization of resource distribution. The system is tested through simulations to evaluate performance metrics such as efficiency, responsiveness, and reliability. [9, 10]. Training datasets are collected from real-world energy consumption patterns. The proposed framework is simulated using Blynk. Blynk was chosen because of its compatibility with ESP8266 modules, capability for real-time cloud data visualization, and ease of integration with IoT hardware. It improves user accessibility and makes performance monitoring dashboards easy to use without requiring the creation of unique mobile apps. Comparative analysis with traditional energy
management systems is conducted. A pilot implementation of the framework is conducted in a controlled environment, such as a micro grid or smart home setup, to evaluate real-world applicability and validate simulation results. The obtained results are analysed, and necessary modifications are made to enhance the efficiency, reliability, and security of the system [11]. Recommendations for future improvements and large-scale deployment strategies are provided. This research methodology ensures a systematic and scientific approach to developing a robust, scalable, and secure IoT-based Smart Energy Management system for Smart Grids [12, 13].

Fig. 2 Key hardware components of IoT-based smart energy monitoring system.

Fig. 2. Key hardware components of IoT-based smart energy monitoring system.

1. NodeMCU ESP8266 Wi-Fi

The original version of this paper is available on https://www.scientific.net/EI.15.43.pdf

The NodeMCU ESP8266 Wi-Fi Module is a small development board that is perfect for Internet of Things projects. A Wi-Fi-enabled microcontroller that wirelessly transmits the processed energy data from the Arduino to a cloud platform. [26]

2. Non-Invasive Current Sensor (SCT-013-000):

The original version of this paper is available on https://www.scientific.net/EI.15.43.pdf

A clamp-on current sensor that detects AC current without making direct electrical contact is a non-invasive current sensor (YHDC SCT-013-000). It offers a practical and secure means of keeping an eye on electrical systems' energy usage [27].

3. Arduino UNO R3 SMD

The original version of this paper is available on https://www.scientific.net/EI.15.43.pdf

A microcontroller board built on the ATmega328P (SMD version) is the Arduino UNO R3 SMD variant. It has six analog inputs and fourteen digital I/O ports. [28]

4. Energy Meter Circuit

The original version of this paper is available on https://www.scientific.net/EI.15.43.pdf

The Energy Meter Circuit combines the NodeMCU, current sensor, and Arduino with capacitors for power stability, resistors for signal scaling, and wires for interface. This configuration effectively measures, processes, and communicates energy usage data.

Fig. 3(a)

Fig. 3. (a)

Fig. 3(b)

Fig. 3. (b)

Fig. 3(a). Wiring Diagram of the IoT-Based Energy Monitoring Circuit Fig. 3(b). Experimental Prototype of IoT-Based Energy Monitoring System

The real hardware prototype, which consists of an Arduino UNO, NodeMCU ESP8266, and SCT013 current sensor constructed on a breadboard, is shown in Fig. 3(a). The system's full wiring schematic is shown in Fig. 3(b) for clarity. It displays the power and signal connections between the microcontroller boards, resistors, capacitor, and sensor. The structure and implementation of the energy monitoring circuit for real-time data collecting are made easier to understand thanks to this graphic representation.

Fig. 4 Block diagram of the data processing workflow in the IoT-based energy monitoring system.

Fig. 4. Block diagram of the data processing workflow in the IoT-based energy monitoring system.

The data processing workflow of an IoT-based smart energy monitoring system is depicted in the flowchart (Fig. 4). A breadboard-based signal conditioning circuit is used to condition the analog signal produced by the Non-Invasive Current Sensor (YHDC SCT-013-000), which detects AC current. After processing the conditioned signal, the Arduino Uno computes the current, power, and energy usage. The NodeMCU (ESP8266), a Wi-Fi module for wireless connectivity, receives the processed data after that. The NodeMCU allows for real-time data storage and display by sending the processed data to a cloud platform like Google Sheets or Blynk. This makes it possible to remotely monitor and analyse patterns in energy consumption. Applications for the smart grid operate more efficiently thanks to this methodical approach, which also improves energy management and optimization.

Results

The original version of this paper is available on https://www.scientific.net/EI.15.43.pdf

Fig. 5 Flowchart of the IoT-based energy monitoring system from data acquisition, processing, to cloud transmission.

Fig. 5. Flowchart of the IoT-based energy monitoring system from data acquisition, processing, to cloud transmission.

This flow diagram (Fig. 5) depicts the operational logic of an IoT-based system, likely for energy monitoring or management. The diagram outlines the steps involved in:

Data Acquisition: Reading sensor data.

Data Processing: Converting sensor data to voltage, calculating current and power, and updating energy consumption.

Data Transmission: Determining when to transmit data and sending it to Google Sheets and a serial monitor.

Observations and Considerations:

The original version of this paper is available on https://www.scientific.net/EI.15.43.pdf

Real-Time Monitoring: The loop between "Calculate Current and Power" and the "Is it Time to Transmit Data?" decision point indicates that the system is performing real-time monitoring and processing.

Data Logging: Sending data to Google Sheets implies that the system is logging data for analysis and historical records.

Flexibility: The "Is it Time to Transmit Data?" decision point provides flexibility in how often data is sent.

Potential Enhancements:

Error Handling: The diagram doesn't show any error handling. For example, what happens if sensor data is invalid or transmission fails?

Data Validation: Adding data validation steps would improve the reliability of the system.

User Interface: The diagram doesn't show how users interact with the system.

Specific Sensors: The diagram doesn't specify the types of sensors used.

The flow diagram (Fig. 5) represents a basic yet functional IoT system for energy monitoring. It highlights the core steps involved in data acquisition, processing, and transmission. Adding error handling, data validation, and user interface considerations would further enhance the system's robustness and usability.

Table 1. Unit consumption (kWh) by building type as measured by Energy Meter

S. No | Type of Building | Unit Consumption (kWh)
1 | Single | 2.82
2 | Double | 11.34
3 | Triple | 19.64

The unit consumption numbers (Table 1) reveal a significant difference in energy usage between single, double, and triple-unit constructions. A single-unit building consumes 2.82 kWh , while a twounit building uses 11.34 kWh . Similarly, a triple-unit building uses 19.64 kWh . This mismatch suggests that energy demand increases disproportionately with building size, most likely due to the growing number of electrical appliances, lights, and heating/cooling systems in multi-unit complexes.

Table 2. Conventional load for single storey house.

S. No.
Load type
Load of one appliance (W)
Hours
No's of Appliances
Total load of the building (Wh)

1
Tube light
45
6
5
1350

2
Ceiling fans
80
8
5
3200

3
LED TV
70
1
1
70

4
Exhaust fans
45
2
1
90

5
LED lights
25
10
8
2000

6
Refrigerator
150
24
1
3600

7
Water Motor
1000
.5
1
500

Total

10710
10.71kWh

Let's analyze "Table 2 Load Calculation - Conventional Load - Single Storey House" in the context. Table 1 provides a useful estimate of the conventional electrical load for a single-story house. A conventional single-story house's computed daily energy consumption is 10.71 kWh , based on the projected power ratings and usage hours of various household appliances. However, the energy meter measures energy consumption at 2.82 kWh , which is much lower. This mismatch occurs because the computation assumes that all appliances function for their entire projected hours, when actual usage varies throughout the day.

Table 3. Conventional load for double storey house.

S. no.
Load type
Load of one appliance (W)
Hours
No's of Appliances
Total load of the building (Wh)

1
Tube light
45
8
9
3240

2
Ceiling fans
90
8
10
7200

3
LED TV
90
6
2
1080

4
Exhaust fans
45
2
2
180

5
LED lights
25
8
18
3600

6
Refrigerator
150
24
1
3600

7
Water Motor
1000
1
1
1000

Total

19900
19.9kWh

A conventional double-story house's daily energy consumption is calculated to be 19.9 kWh based on the expected power ratings and usage hours of various household appliances. However, the energy meter measures energy use at 11.34 kWh , which is much lower. This discrepancy occurs because the computation assumes that all appliances operate for the full predicted duration, whereas actual usage differs.

Table 4. Conventional load for triple storey house.

CONVENTIONAL LOAD TRIPLE STOREY

Load type
Load of one appliance (W)
Hours
No's of Appliances
Total load of the building (Wh)

1
Tube light
46
8
18
6624

2
Ceiling fans
88
5
16
7040

3
LED TV
70
8
5
2800

4
Exhaust fans
55
2
3
330

5
Ceiling light
60
10
24
14400

7
Refrigerator
180
24
3
12960

8
Water Motor
1000
1.5
2
3000

Total

47154
47.15kWh

The calculated daily energy consumption of a conventional triple-storey house is 47.15 kWh , based on the estimated power ratings and usage hours of various household appliances. However, the measured energy consumption from the energy meter shows 19.64 kWh , which is significantly lower. In conclusion, comparison of estimated and measured energy consumption for single, double, and triple-story dwellings reveals the disparity between theoretical estimates and actual usage. While calculations imply that all appliances run continuously, actual consumption is much lower due to occasional appliance operation, user behaviour, and energy-saving efforts. The energy meter gives real-time information on actual usage patterns, highlighting the significance of data-driven monitoring for accurate energy management. These findings emphasize the need of efficient energy planning and IoT-based smart monitoring systems for optimizing electricity consumption and reducing waste.

Discussion

The original version of this paper is available on https://www.scientific.net/EI.15.43.pdf

The results presented in this article provide valuable insights into energy consumption patterns in Pakistan, which highlight the potential for IoT-based smart energy management systems to address the region's energy challenges. The analysis of household energy consumption revealed a variable relationship between house size and energy usage. While larger houses generally consume more energy, there were significant variations within the dataset. This suggests that factors beyond house size, such as occupancy levels, appliance types, and usage habits, play a crucial role in determining energy consumption [14, 15, 24]. The load calculations for single, double, and triple-story houses showed a surprising consistency in total load, despite the increasing size of the dwellings. This unexpected trend raises questions about the accuracy of the data or potential variations in appliance usage patterns across different house types [16, 22, 23].
The flow diagram outlining the IoT-based energy monitoring system demonstrates the potential for real-time data acquisition, processing, and transmission. The system's ability to send data to a cloud platform such as Google Sheets enables remote monitoring and analysis, which can be particularly valuable in areas with limited accessibility [17, 22]. The implementation of IoT-based smart energy management systems offers numerous benefits. Real-time monitoring and analysis of energy consumption data enable better management of the electricity grid, particularly during peak demand periods. By understanding energy usage patterns, residents can be encouraged to adopt energy-saving practices and invest in energy-efficient appliances [18, 19]. Additionally, these systems help balance
the load on the grid by optimizing appliance usage and promoting energy conservation during peak hours. As a result, optimizing energy consumption can lead to a significant reduction in electricity bills for residents [20]. The study acknowledges certain limitations in the data, including potential inconsistencies and the need for further validation.

Future Recommendations

The original version of this paper is available on https://www.scientific.net/EI.15.43.pdf

Future research should prioritize enhancing the accuracy and consistency of energy consumption data through improved measurement techniques and validation processes. Additionally, incorporating contextual information such as occupancy levels, appliance types, and usage habits can provide a more comprehensive understanding of energy consumption patterns [21,25]. Another crucial area of focus is evaluating the scalability of the IoT-based system for a larger population and exploring its integration with existing energy infrastructure. Lastly, a detailed cost-benefit analysis is essential to evaluate economic viability.

Conclusion

The original version of this paper is available on https://www.scientific.net/EI.15.43.pdf

This study highlights the transformative potential of IoT-powered solutions for optimizing energy usage, improving grid dependability, and promoting sustainability in residential settings. The large difference between calculated and measured energy use in single, double, and triple-story structures highlights the necessity of real-time monitoring to capture actual usage patterns, allowing for more accurate energy planning and waste reduction. The suggested Smart Energy Monitoring System provides predictive analytics, significant cost savings, improved load balancing, and safe data management. While constraints such as data discrepancies and scalability issues persist, the findings lay a solid platform for future improvements, facilitating broader adoption wider implementation in smart grid frameworks and contributing to a more efficient and sustainable energy economy.

References

The original version of this paper is available on https://www.scientific.net/EI.15.43.pdf

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Alahakoon, D., & Yu, X. 2016. Smart Electricity Meter Data Intelligence for Future Energy Systems: A Survey. IEEE Transactions on Industrial Informatics, 12(1), 425-436.

Khan, S., et al. 2017. Industrial Internet of Things: Recent Advances, Enabling Technologies and Open Challenges. Computers & Electrical Engineering, 64, 2-20.

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Arruda, Roberto Nonato De, Figueiredo, Karoline V., Vasco, D., Haddad, A., and Najjar, Mohammad K.. 2023. "Cost-benefit analysis of solar energy integration in buildings: a case study of affordable housing in Brazil". Frontiers in Built Environment.

Hassan, Mohammed Tarek, Abdelgeliel, M., and Hamad, M.. 2022. "ENERGY audit and management on residential and commercial building: A case study". IEEE International Conference on Renewable Energy Research and Applications.

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Cheng, Qian. 2025. "Application and Analysis of Renewable Energy in Building Energy Efficiency". Applied and Computational Engineering.

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This ePub file was created using machine-generated methods and automated software to convert the source text into an ePub format and may contain formatting inconsistencies from the original version. Formatting may vary depending on the device used.

The original version of this eBook is available on https://www.scientific.net/book/engineering-innovations-vol-15/978-3-0364-1968-8

Engineering Innovations, ISSN: 2813-1002, Vol. 15, pp 53-64

doi: 10.4028/p-Jcv2A8

© 2025 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2025-01-25

Revised:
2025-02-05

Accepted:
2025-09-30

Online:
2025-11-18

Impact of Artificial Intelligence on Management Control Processes

The original version of this paper is available on https://www.scientific.net/EI.15.53.pdf

NOBACH Kai , PETRIASHVILI Lily

Nuremberg Institute of Technology Georg Simon Ohm, Nuremberg, Germany

Georgian Technical University, Tbilisi, Georgia

kai.nobach@th-nuernberg.de, .petriashvili@gtu.ge

*Corresponding author: I.petriashvili@gtu.ge

Keywords: management control, artificial intelligence, predictive AI, optimization AI, generative AI.

Abstract

Digital transformation has developed significantly in the 21st century, and special attention has been paid to it in business and society. It is becoming more relevant and presents the private and public sectors with new challenges. Computer and mobile devices, information, and communication technologies are actively used in social, economic, industrial, engineering, and other fields, to create and implement their respective new-generation software applications. New paradigms based on the software industry including the development, and creation of new methods and methodologies or perfecting the existing ones are receiving a lot of attention worldwide, on which artificial intelligence (AI) had a significant impact. Regarding the ongoing digital transformation of organizations and business models companies have been facing the challenge for years of digitalizing the processes in management control. These processes have been undergoing fundamental changes, driven by the rapid development of AI in recent years. In this context, one of the most innovative and significant technological breakthroughs was the development of generative AI. AI tool ChatGPT plays an important role in advancing scientific progress by promoting the use of artificial intelligence, improving user interaction and accelerating innovation in various industries. This paper explores how AI can increase the effectiveness and efficiency of decision-making and management control. Efficiency is achieved through strategic decisions and efficiency through operational decisions. By integrating AI technologies, organizations can automate repetitive tasks, streamline data processes, and improve financial reporting and forecasting accuracy. AI-based analytics provide managers with deeper insights that enable more informed decisions about resources, processes, products and services. In addition, the paper examines how AI has shifted organizational focus from operational efficiency to strategic priorities. This change has contributed to a more flexible and responsive management control framework that allows organizations to adapt quickly their control system to changing market conditions and maintain a competitive advantage. In addition, one of the most prominent fields today is nanomanufacturing and the optimization of production processes. Through AI-driven optimization, it becomes possible to refine the synthesis and assembly of nanostructures, significantly improving precision and efficiency in production.

1. Introduction

The original version of this paper is available on https://www.scientific.net/EI.15.53.pdf

In the current era of digital transformation, characterized by exponential growth of big data, it remains a challenge for organizations to use sophisticated analytical methodologies and advanced AI algorithms to gain an edge in the competitive environment by improving management decisions.

It is worth noting that the COVID-19 pandemic has also precipitated profound global economic, social and technological transformations, significantly driven by the digitization of processes. In an era where approximately 15 zettabytes of information are generated monthly, traditional methods and technologies have proven inadequate for storing, processing, and analyzing such vast amounts of data.

Traditional data management methods and decision-making tools are becoming less effective, emphasizing the need for advanced AI-based solutions. This study explores the impact of three specific AI approaches on management control processes:

Predictive AI: Prediction of future business developments based on historical data

Optimization AI: Improving the quality of business data and increasing reporting speed

Generative AI: Creation of various types of content, such as text, audio, imagery and synthetic data

These tools play a crucial role in improving decision-making, optimizing resources and processes and enhancing strategic planning within management control systems. The ability of AI to process large amounts of data, identify patterns, and create predictive models has revolutionized traditional management control concepts such as budgeting, management reporting, performance measurement, investment analysis, and risk management.

In today's ongoing business processes, the emergence of AI has brought revolutionary changes and has achieved significant results in almost all areas. AI has increased efficiency and productivity by automating repetitive and daily tasks, allowing managers to focus more on strategic and creative activities rather than on daily routines. Automating ongoing processes enables the rapid and errorfree processes of various operations. It enhances innovations by providing new ways to solve complex problems, which makes it possible to create new products and services and strengthen existing ones. By the use of AI, companies can develop new business models, find the latest solutions for customer needs, and stay ahead of competitors.

In this paper, we will first explore how predictive AI can enhance planning capabilities and facilitates accurate financial forecasts; we also explore AI-based optimization for nanotechnology, focusing on improving nanostructure design, simplifying manufacturing processes, and improving accuracy and efficiency in applications. We then present the opportunities that optimization AI offers to improve investment decisions, cost simulations and risk management processes, thereby supporting business-partnering activities. In addition, we analyze the potentials of generative AI to automate complex data management and reporting tasks, optimize planning procedures and offer creative approaches to problem solving [1-6].

The schematic diagram below (Fig. 1) depicts the path of influence of AI on management control processes and outcomes and points to the potential challenges that need to be considered for the effective deployment of AI.

Fig. 1. Meaning and consequences of AI for management control processes.

Fig. 1. Meaning and consequences of AI for management control processes.

To address these challenges and make the most of business data, AI has become a key solution. With its advanced ability to handle vast amounts of information, AI provides valuable insights and drives innovation across different industries. By adopting AI, companies can not only keep up with the digital transformation but also unlock new opportunities for growth and efficiency.

In this rapidly evolving landscape, the function of management control becomes increasingly vital. Management accountants and controllers integrate AI-based data analytics into the organizational framework, ensuring that strategic and operational goals are met. This function encompasses planning, analyzing and consulting activities, which are crucial for aligning AI-driven insights with business objectives. By coordinating planning processes, tracking performance, and addressing deviations promptly, controller enhance decision-making processes, support strategic planning, and ultimately drive organizational success.

Through our research, we have identified the three AI types mentioned above and analyzed their validity on the management control processes to support managerial decisions.

One of the directions of our research is determining which approaches are best suited to optimize the specific management control processes. The major characteristics and application areas of each of the three types of AI are illustrated in the following overview (Fig. 2).

Fig. 2. Different types and applications of AI.

Fig. 2. Different types and applications of AI.

Predictive AI models are created based on historical data to forecast future events or unknown outcomes. These tools analyze existing data to find patterns and relationships that can be used to predict future data points. Advanced models apply machine learning algorithms and statistical models to help analyze historical data and make predictions about future events.

The working principle of predictive modelling is as follows:

Collection of data, e.g. historical volume data, previous quality and service information, time data, costs and revenues, geographic data, weather data, demographics and traffic data.

Data processing and cleaning, e.g. validation of data, identification and elimination of anomalies that may lead to distortion of forecasts.

Development and optimization of the algorithm.

Common approaches used in predictive AI include regression models, classification models, and time series models that analyze data collected or sequenced over time. The created model is then also evaluated, i.e. determining how close the predictions are to the actual values.

Optimization AI leverages smart data technologies to solve complex problems by finding the most effective solutions, whether it is cutting costs, time of service, or boosting efficiency. This process involves setting an objective, exploring potential solutions through advanced algorithms, and working within certain constraints. It is widely used in business and financial management to improve decision-making and streamline operations [7-13].

Our research also addresses the rather new technologies of Generative AI, which have a transformative impact on both scientific and business spheres. Generative AI has completely changed perceptions and views in scientific fields and social environments. At its core, it refers to AI models capable of creating new content, be it text, images, music, video compositions or code that closely mimics human-generated work. It generates texts, can write stories, or even engages in conversation.

Regarding management control processes generative AI can not only competently analyze data sets and make predictions, but also independently comment on business developments and deviations from targets and create proposals for actions.

In the dynamic world of communications, AI is revolutionizing the way businesses connect with their audiences by automating critical processes across multiple domains. Communication workflows are becoming more sophisticated through AI automation. Management accountants and controllers can now implement professional chatbots that seamlessly handle information requests and accurately reflect business developments in a predictable manner. These automated systems can trigger personalized notifications based on business developments, ensuring that acute management needs for action are not overlooked.

In addition, data analysis, which was once a time-consuming and complex task, is now simplified with AI-powered tools. Finance teams can now access real-time performance metrics, conduct business analysis, gather competitive intelligence, and create comprehensive reports with unprecedented speed and accuracy. The ability to track, measure, and optimize management decisions has never been more powerful.

Recommender engines can predict business developments, propose adaptive content strategies, and offer dynamic management measures to achieve specific business targets. These technologies help to ensure that management interactions are relevant, timely, and tailored.

2. Methodology

The original version of this paper is available on https://www.scientific.net/EI.15.53.pdf

In this study, we used a literature review and case study methodology to examine the impact of AI on management control processes, specifically focusing on the application of these technologies to improve the effectiveness and efficiency of communication and decision-making.

Given the rapid development of AI tools and their significant influence on strategic business decisions and operational efficiency, we sought to understand these impacts through a detailed analysis of online articles and existing literature.

The research was conducted by reviewing a wide range of online articles, scientific papers, and industry reports published between 2019 and 2024. To ensure reliability, only peer-reviewed journals, reputable industry publications, and well-reviewed academic papers were included in the review.

The process involved searching major academic databases such as Google Scholar, Scopus, and Web of Science using keywords such as "Artificial Intelligence", "Management Control", "Decision Making", "Predictive AI", "Optimization AI", and "Generative AI".

The literature review methodology allowed us to collect a wide range of perspectives on the impact of AI on management control processes.

The papers were chosen to represent a variety of sectors, including general business management, finance and management control, to provide a comprehensive view of the use of AI in these areas.

3. Nature and Meaning of Management Control Processes

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The function of management control, also known as "controlling" in German-speaking countries, is a fundamental management task in companies and organizations. It focuses on planning, analyzing, steering and providing business information to support decision-making and goal achievement. By integrating financial and non-financial data, management control provides transparency about the
business situation and development of the company and helps to optimize strategic and operational processes.

Management control activities include gathering, planning, and analyzing and interpreting business information for decision-making processes. These controlling activities play an important role in the coordination of different departments within the company. Thus, management control is a management function focusing on the improvement of operational processes and strategic decisions.

In order to carry out this task effectively and efficiently, various controlling activities are required, which can be structured and designed using the process model of the International Group of Controlling. According to this model, the following main processes of management control can be distinguished:

Strategic planning

Planning, budgeting and forecasting

Investment controlling

Cost accounting

Management reporting

Business partnering

Project controlling

Risk controlling

Data management

Enhancement (of organization, processes, instruments and systems)

The following explanations investigate whether and to what extend these processes can be optimized by applying different AI technologies. Therefore, specific use cases were developed to analyze how selected management control and decision-making processes can be designed effectively and efficiently with the help of predictive, optimization and generative AIs.

4. Application of AI for Management Control Processes

The original version of this paper is available on https://www.scientific.net/EI.15.53.pdf

AI can be applied in different ways to improve management control activities and decision-making processes. In the following, we will first show how predictive AI can be used to create meaningful time and cost forecasts for a specific use case. We then examine the possibilities of optimization AI and generative AI to solve certain tasks of management control.

4.1. Application of predictive AI

The chosen application example is about predicting the duration and total cost of shipping 12,000 tons of cargo from China to Germany via Georgia, assuming a cost of per ton per day. Using a predictive AI tool, an attempt was made to estimate the required duration of transportation and the total cost of transporting the entire cargo. The following table shows the necessary steps and key components for preparing the forecast (Tab. 1).

Table 1. Steps and key components of creating the prediction model.

Steps and Components | Description
Data collection | Gathering historical shipment data, geographical data, weather data, traffic data, and customs data
Data processes | Cleaning and preparing the data by removing outliers, filling missing values, and normalizing the data
Feature engineering | Transforming raw data into meaningful features like distance, temporal features, and transportation specifics
Model training | Using machine learning models such as regression, classification, and time series models to learn from historical data
Model evaluation | Assessing model performance using metrics like accuracy, mean absolute error and root mean squared error
Prediction | Generating predictions for travel times, costs, and potential delays based on historical and real-time data
Continuous learning | Continuously retraining the models as new data becomes available to improve accuracy and adaptability
Example scenario | Describing the process of predicting transportation details for cargo from China to Germany via Georgia
Benefits | Highlighting the advantages such as increased accuracy, cost savings, improved efficiency, and risk mitigation

A Python program was created to forecast the estimated transport time and costs (Fig. 3). The model predicted a travel time of 16.94 days and total travel costs of for the distance of , assuming a transport speed of 800 km per day.

Define the given parameters
distance_km = 11000 # distance in kilometers
speed_km_per_day = 800 # speed in km per day
delay_days = 3 # additional delay in days
cost_per_ton_per_day = 250 # cost in dollars per ton per day
cargo_tons = 12000 # cargo in tons
# Calculate the predicted travel time (excluding delay)
predicted_travel_time_days = distance_km / speed_km_per_day
# Add the delay to the travel time
total_travel_time_days = predicted_travel_time_days + delay_days
# Calculate the total cost
total_cost = total_travel_time_days * cargo_tons *
    cost_per_ton_per_day
# Return the results
total travel time days, total cost

Fig. 3. Python model to predict duration and costs of sea cargo.

In result of the research, it can be stated that predictive AI is well suited for automating, optimizing planning, and cost accounting processes. In addition, the forecast values generated can be included in management reporting and improve risk controlling.

4.2. Application of optimization AI

To analyze the effectiveness of optimization AI, a specific decision situation was considered, in which the aim was to minimize the costs for a distribution network through an optimal choice of warehouse
locations. Regarding this use case, a logistics manager of a large e-commerce company operating in 5 major countries had to find the optimal warehouse layout to minimize overall costs and maintain a high level of customer service. The following initial parameters should be taken into account in the decision-making process:

Package distribution in 5 major cities: ,D , and E

Customer demand (packages per day) in each city:

A maximum of 3 warehouses can be built

Fixed cost of building a warehouse:

Operating cost per package:

Transportation cost per package km: $0.1

Service level requirement: of packages must be delivered within 1 day

The goal of using the optimization AI approach was to reduce the total costs (fixed costs + operating costs + transportation costs) as well as the service time. At the beginning, a distance matrix between cities was created that allowed multiple distances in km (Tab. 2):

Table 2. Distance matrix between cities.

| A | B | C | D | E
A | 0 | 200 | 300 | 400 | 500
B | 200 | 0 | 250 | 350 | 450
C | 300 | 250 | 0 | 200 | 400
D | 400 | 350 | 200 | 0 | 300
E | 500 | 450 | 400 | 300 | 0

To solve the optimization problem the linear programming was chosen, which was implemented through Python (Fig. 4).

# Define cities and demands
cities = ['A', 'B', 'C', 'D', 'E']
demand = {'A': 1000, 'B': 1500, 'C': 800, 'D': 1200, 'E': 500}
# Define distances (in km)
distances = {
    ('A','A'): 0, ('A','B'): 200,('A', 'C'): 300,('A', 'D'): 400,('A','E'
        ): 500,
    ('B', 'A'): 200, ('B', 'B'): 0, ('B', 'C'): 250, ('B', 'D'): 350, ('B', 'E'
        ): 450,
    ('C'. 'A'): 300, ('C', 'B'): 250, ('C', 'C'): 0, ('C', 'D'): 200, ('C', 'E'
        ): 400,
    ('D', 'A'): 400, ('D', 'B'): 350, ('D', 'C'): 200, ('D', 'D'): 0, ('D', 'E'
        ): 300,
    ('E', 'A'): 500, ('E', 'B'): 450, ('E', 'C'): 400, ('E', 'D'): 300, ('E',
        'E'): 0
}
# Cost parameters
fixed_cost = 1000000
operating_cost = 2
shipping_cost = 0.1

Fig. 4. Python code to optimize warehouse locations.

With this approach, the cities B and D were identified as optimal warehouse locations. This solution meets all constraints, as only 2 warehouses are required (less than the maximum of 3 ) and all cities are within a 1 -day shipping distance.

The Optimization AI provides this solution along with sensitivity analysis, showing how the solution might change if certain parameters (like shipping costs or demand) were to change. As a result, the optimal warehouse locations and a shipping plan are issued.

The AI application has forecast total costs of for the construction of the distribution network (Fig. 5).

Output
Optimal Warehouse Locations:
Warehouse in city B
Warehouse in city D
Shipping Plan:
Ship 1000.0 units from B to A
Ship 1500.0 units from B to B
Ship 800.0 units from D to C
Ship 1200.0 units from D to D
Ship 500.0 units from D to E
Total Cost: $2306000.00

Fig. 5. Output of Python model to optimize warehouse locations.

The optimization algorithm chose the cities B and D as these are centrally located and are suitable for minimizing overall shipping distances. The warehouses in the two cities can cover all other cities within reasonable distances. In addition, using two warehouses instead of three saves on fixed costs.

This solution demonstrates how optimization AI can solve complex decision problems, balancing multiple factors to find the most cost-effective solution. In result of the conducted research, it can be stated that optimization AI offers improvement potential for strategic planning, investment control and cost estimation as well as for business partnering, project controlling and risk management.

4.3. Application of generative AI

The use of generative AI offers further opportunities for automating and optimizing management control processes. Our research in this field focused on the BART technology (Bidirectional and AutoRegressive Transformers). This progressive approach combines an autoregressive decoder (such as GPT) and a bidirectional encoder (as in Bidirectional Encoder Representations from Transformers). The algorithm can solve various linguistic tasks, recover damaged text, talk in interactive mode, write short content and translate texts into different languages. Most importantly, it can create coherent and contextually relevant text, which is very important for applications such as text analysis, dialog systems and automatic content creating that can be used effectively in managing business processes.

Fig. 6. Python code using the BART model to analyze and summarize text of reports.

Fig. 6. Python code using the BART model to analyze and summarize text of reports.

As part of management control processes, the BART technology can be applied to carry out automated analysis of business reports for example. To illustrate BART's performance, an analysis of Siemens Energy's business development was carried out based on the annual report for the fiscal year
2023. The document has 122,683 words on 201 pages. The generative AI processes this text to create a summary, and the length of the original text and the summary are displayed as part of the output (Fig. 6).

When the code is run, it summarizes the input text, showing how generative AI can be used to extract key information from longer documents automatically. The analysis of Siemens Energy's 2023 annual report revealed the following financial information and strategic developments.

The revenue increased by to 31.1 billion euros. However, the company posted a net loss of billion, mainly due to quality and cost issues at Siemens Gamesa. Despite these challenges, orders ( 112 billion euros) and positive free cash flow ( 784 million euros) are promising indicators.

In addition, we can also get a brief analysis of operational and strategic developments, market and environmental context, research and development (R&D), forward outlook, risks, and mitigation.

The Python code provided uses the BART model to summarize a long text within Siemens Energy's annual report. As mentioned above, the original text length was 122,683 words. After summarizing, the length became only 475 words. Summary length and content may vary slightly depending on the exact processes, but this gives us a good idea of what to expect. The BART model effectively condenses the original text into a concise summary that outlines key points.

It can be said that the output of generative AI is completely original and functional. It is especially relevant for improving management reporting, business partnering, data management and further development of management control processes. The technology can also be applied to summarize relevant data form management reports and other documents to accelerate and improve planning and forecasting processes.

The three considered AI categories have a particularly profound impact on management control processes. The following table summarizes the main possible applications of the AI technologies examined (Tab. 3).

Table 3. Possible applications of AI technologies for management control processes.

Processes of management control | Predictive AI | Optimization AI | Generative AI
Strategic Planning | √ | √ | √
Planning, Budgeting and Forecasting | √ |  | √
Investment Controlling |  | √ |
Cost Accounting | √ | √ |
Management Reporting | √ |  | √
Business Partnering | √ | √ | √
Project Controlling |  | √ | √
Risk Controlling | √ | √ | √
Data Management |  |  | √
Enhancement (of Organization, Processes, Instruments and Systems) |  |  | √

Predictive AI has especially revolutionized planning and forecasting. By analyzing historical data and identifying patterns, predictive AI models can anticipate future trends, recognize potential risks and reveal opportunities with remarkable accuracy. This allows companies to make decisions that are more informed and develop proactive strategies.

Using predictive modeling and performance optimization techniques, artificial intelligence enables researchers to design more sophisticated nanodevices with reduced development costs and accelerated innovation. These improvements create opportunities for more targeted and efficient technology applications in many scientific and industrial domains.

Optimization AI has transformed resource allocation and process efficiency. These algorithms can analyze complex systems and identify optimal solutions for resource distribution, scheduling, and investment decisions. As a result, companies can achieve significant cost reductions and operational improvements [14-16].

The emergence of generative AI represents the latest frontier in AI's impact on management control. These tools can create new content, designs, and even strategic plans based on learned patterns. This technology is beginning to assist in scenario planning, report generation, and even creative problem solving in management contexts.

The integration of these AI technologies has led to more data-driven, agile, and adaptive management control processes. Companies leveraging AI have reported increased accuracy in financial forecasting, improved risk management, and enhanced decision-making capabilities.

However, this AI-driven transformation also presents challenges, including ethical considerations, data privacy concerns, and the need for upskilling the workforce. As AI continues to evolve, its impact on management control processes is expected to deepen, necessitating ongoing research and adaptation in both academic and practical spheres [17-25].

5. Conclusions

The original version of this paper is available on https://www.scientific.net/EI.15.53.pdf

The study highlights the transformative impact of artificial intelligence (AI) on management control processes, demonstrating its potential to enhance both strategic and operational decisionmaking in organizations.

AI-driven management control systems are critical to meeting the challenges posed by the exponential growth of big data and the rapid pace of digital transformation. By automating routine tasks and providing deeper insights, AI enables management professionals to focus on strategic priorities, facilitating flexibility and responsiveness to dynamic market conditions.

AI optimization in nanotechnology accelerates innovation through precision design, efficient manufacturing and advanced materials synthesis. It lowers development costs, improves predictive modeling, and helps researchers create nanodevices that are more sophisticated with greater accuracy and speed.

This study shows that AI technologies not only increase efficiency, but also enable companies to maintain a competitive advantage by driving innovation, improving resource allocation, and adapting to the evolving business environment. As organizations continue to integrate AI into their management frameworks, its role in shaping future management control practices will become increasingly essential.

To further enhance the impact of this study, there are recommended to conduct: (1) Scalability insights (investigating how small and medium-sized enterprises can adopt and scale AI solutions in management control to ensure inclusivity) and (2) Quantitative evaluation (including quantitative results from AI implementations to strengthen the empirical evidence and validate the findings).

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Keywords Index

A

Artificial Intelligence Impact of Artificial Intelligence on Management Control Processes

As7341 Sensor Portable VIS-NIR Spectrophotometer for Detecting Adulteration of Minced Beef and Chicken Using AS7341 Sensor with PCA Method

B

Beef Portable VIS-NIR Spectrophotometer for Detecting Adulteration of Minced Beef and Chicken Using AS7341 Sensor with PCA Method

C

Chicken Portable VIS-NIR Spectrophotometer for Detecting Adulteration of Minced Beef and Chicken Using AS7341 Sensor with PCA Method

D

Dislocation Model Analysis of the Dislocation Structure, Kinetics and Dynamics in the High-Entropy Alloy

E

Energy Efficiency Development of an loT-Enabled Smart Home Automation System

Energy Management IoT-Based Smart Energy Management for Smart Grids Applied in a Home

Esp8266 Development of an loT-Enabled Smart Home Automation System

F

Fault Diagnosis Fault Detection in Spur Gears Through Vibration Signal Analysis and Machine Learning Techniques

G

Gears Condition Monitoring Fault Detection in Spur Gears Through Vibration Signal Analysis and Machine Learning Techniques

Generative Ai Impact of Artificial Intelligence on Management Control Processes

H

High Entropy Alloy; Acoustic Relaxation; Plastic Deformation Analysis of the Dislocation Structure, Kinetics and Dynamics in the High-Entropy Alloy

Home Automation Development of an loT-Enabled Smart Home Automation System

I

Internet Of Things Development of an loT-Enabled Smart Home Automation System

Iot IoT-Based Smart Energy Management for Smart Grids Applied in a Home

M

Machine Learning Fault Detection in Spur Gears Through Vibration Signal Analysis and Machine Learning Techniques

Management Control Impact of Artificial Intelligence on Management Control Processes

Mobile Application Development of an loT-Enabled Smart Home Automation System

O

Optimization Ai Impact of Artificial Intelligence on Management Control Processes

P

Pca Portable VIS-NIR Spectrophotometer for Detecting Adulteration of Minced Beef and Chicken Using AS7341 Sensor with PCA Method

Pork Portable VIS-NIR Spectrophotometer for Detecting Adulteration of Minced Beef and Chicken Using AS7341 Sensor with PCA Method

Predictive Ai Impact of Artificial Intelligence on Management Control Processes

S

Scalograms Fault Detection in Spur Gears Through Vibration Signal Analysis and Machine Learning Techniques

Sensors Development of an loT-Enabled Smart Home Automation System

Short-time Fourier Transform Fault Detection in Spur Gears Through Vibration Signal Analysis and Machine Learning Techniques

Smart Energy Power IoT-Based Smart Energy Management for Smart Grids Applied in a Home

Smart Grids IoT-Based Smart Energy Management for Smart Grids Applied in a Home

Spectrophotometer Portable VIS-NIR Spectrophotometer for Detecting Adulteration of Minced Beef and Chicken Using AS7341 Sensor with PCA Method

X

X-ray Structural Analysis Analysis of the Dislocation Structure, Kinetics and Dynamics in the High-Entropy Alloy

Authors Index

A

Ahmad, S. Fault Detection in Spur Gears Through Vibration Signal Analysis and Machine Learning Techniques

Ahmad, W. IoT-Based Smart Energy Management for Smart Grids Applied in a Home

Asif, M. Fault Detection in Spur Gears Through Vibration Signal Analysis and Machine Learning Techniques

Asih, R. Development of an loT-Enabled Smart Home Automation System

B

Bednarchuk, T. Analysis of the Dislocation Structure, Kinetics and Dynamics in the High-Entropy Alloy

E

Endarko, E. Portable VIS-NIR Spectrophotometer for Detecting Adulteration of Minced Beef and Chicken Using AS7341 Sensor with PCA Method

G

Galtsov, N. Analysis of the Dislocation Structure, Kinetics and Dynamics in the High-Entropy Alloy

H

Hashim, U. Development of an loT-Enabled Smart Home Automation System

Hatta, A. Portable VIS-NIR Spectrophotometer for Detecting Adulteration of Minced Beef and Chicken Using AS7341 Sensor with PCA Method

Hurova, D. Analysis of the Dislocation Structure, Kinetics and Dynamics in the High-Entropy Alloy

I

Ibrahim, N. Development of an loT-Enabled Smart Home Automation System

K

Khan, A. Fault Detection in Spur Gears Through Vibration Signal Analysis and Machine Learning Techniques

M

Malik, S. Fault Detection in Spur Gears Through Vibration Signal Analysis and Machine Learning Techniques

Mushtaq, A. IoT-Based Smart Energy Management for Smart Grids Applied in a Home

N

Natsik, V. Analysis of the Dislocation Structure, Kinetics and Dynamics in the High-Entropy Alloy

Nobach, K. Impact of Artificial Intelligence on Management Control Processes

P

Pamungkas, Y. Development of an loT-Enabled Smart Home Automation System

Petriashvili, L. Impact of Artificial Intelligence on Management Control Processes

S

Semerenko, Y. Analysis of the Dislocation Structure, Kinetics and Dynamics in the High-Entropy Alloy

T

Tabachnikova, E. Analysis of the Dislocation Structure, Kinetics and Dynamics in the High-Entropy Alloy

U

Uda, M. Development of an loT-Enabled Smart Home Automation System

W

Wardana, H. Portable VIS-NIR Spectrophotometer for Detecting Adulteration of Minced Beef and Chicken Using AS7341 Sensor with PCA Method

Widyastuti, W. Development of an loT-Enabled Smart Home Automation System

Z

Zinoviev, P. Analysis of the Dislocation Structure, Kinetics and Dynamics in the High-Entropy Alloy

Zoryansky, V. Analysis of the Dislocation Structure, Kinetics and Dynamics in the High-Entropy Alloy

Zulkifli, A. Development of an loT-Enabled Smart Home Automation System