Application of Artificial Neural Networks for Quality Classification in Electromobility Manufacturing Processes
DOI:
https://doi.org/10.4108/dtip.13005Keywords:
quality control, artificial neural networks, machine learning, predictive quality, electromobility, manufacturing process, product classification, data-driven quality managementAbstract
INTRODUCTION: Modern manufacturing requires proactive, data-driven quality control methods that support process stability, reduce non-conformities and improve product reliability.
OBJECTIVES: The objective of this paper is to evaluate the applicability of selected artificial neural network models for quality classification in an electromobility-related manufacturing process.
METHODS: The study used empirical production data, SIPOC process analysis and five neural network models evaluated with accuracy, error rate, validation cost, operational indicators, confusion matrices and ROC/AUC analysis.
RESULTS: The analysed models achieved validation accuracy above 95%, with the Narrow Neural Network obtaining the best overall result of 97.0% accuracy, 3.0% error rate and the lowest validation cost.
CONCLUSION: The results confirm that artificial neural networks can effectively support quality classification and proactive quality management in electromobility manufacturing processes.
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[1] Mikulić, D., Dolaček, Z.: Quality of concrete structures through standards application. Application of Codes, Design and Regulations 1(1), 81–87 (2005).
[2] Wang, J., Liu, X., Wang, X., Jin, K.: Process machining allowance for reliability analysis of mechanical parts based on hidden quality loss. Eksploatacja i Niezawodnosc – Maintenance and Reliability 25(4) (2023).
[3] Riccetti, S.: End product quality control. In: Riccetti, S. (ed.) Designing food safety and equipment reliability through maintenance engineering, pp. 297–324. CRC Press, Boca Raton (2014).
[4] Johanesa, T.V.A., Equeter, L., Mahmoudi, S.A.: Survey on AI applications for product quality control and predictive maintenance in Industry 4.0. Electronics 13(5), 976 (2024). https://doi.org/10.3390/electronics13050976
[5] Papageorgiou, K., Theodosiou, T., Rapti, A., Papageorgiou, E.I., Dimitriou, N., Tzovaras, D., Margetis, G.: A systematic review on machine learning methods for root cause analysis towards zero-defect manufacturing. Frontiers in Manufacturing Technology 2, 972712 (2022). https://doi.org/10.3389/fmtec.2022.972712
[6] Lee, K.J., Kwon, J.W., Min, S., Yoon, J.: Embedding convolution neural network-based defect finder for deployed vision inspector in manufacturing company Frontec. Proceedings of the AAAI Conference on Artificial Intelligence 34(8), 13164–13171 (2020). https://doi.org/10.1609/AAAI.V34I08.7020
[7] Msakni, M.K., Risan, A., Schütz, P.: Using machine learning prediction models for quality control: a case study from the automotive industry. Computational Management Science 20(1), 14 (2023). https://doi.org/10.1007/s10287-023-00448-0
[8] Rožanec, J.M., Bizjak, L., Trajkova, E., Zajec, P., Keizer, J., Fortuna, B., Mladenić, D.: Active learning and novel model calibration measurements for automated visual inspection in manufacturing. Journal of Intelligent Manufacturing 35(5), 1963–1984 (2024). https://doi.org/10.1007/s10845-023-02098-0
[9] Manivannan, S.: Automatic quality inspection in additive manufacturing using semi-supervised deep learning. Journal of Intelligent Manufacturing 34(7), 3091–3108 (2023). https://doi.org/10.1007/s10845-022-02000-4
[10] El Zant, C., Barbar, A.M., Awad, M.: Enhanced manufacturing execution system “MES” through a smart vision system. In: Advances in Intelligent Systems and Computing, pp. 325–334. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-70566-4_52
[11] Eddy, D., White, M., Blanchette, D.: Intelligent insights for manufacturing inspections from efficient image recognition. Machines 11(1), 45 (2023). https://doi.org/10.3390/machines11010045
[12] Cumbajin, E., Rodrigues, N., Costa, P., Miragaia, R., Frazão, L., Costa, N., Fernández-Caballero, A., Carneiro, J., Buruberri, L.H., Pereira, A.: A systematic review on deep learning with CNNs applied to surface defect detection. Journal of Imaging 9(10), 193 (2023). https://doi.org/10.3390/jimaging9100193
[13] Schmitt, J., Bönig, J., Borggräfe, T., Beitinger, G., Deuse, J.: Predictive model-based quality inspection using machine learning and edge cloud computing. Advanced Engineering Informatics 45, 101101 (2020). https://doi.org/10.1016/j.aei.2020.101101
[14] Chung, J., Shen, B., Law, A.C.C., Kong, Z.J.: Reinforcement learning-based defect mitigation for quality assurance of additive manufacturing. Journal of Manufacturing Systems 65, 822–835 (2022). https://doi.org/10.1016/j.jmsy.2022.11.008
[15] Bousdekis, A., Lepenioti, K., Apostolou, D., Mentzas, G.: Data analytics in Quality 4.0: literature review and future research directions. International Journal of Computer Integrated Manufacturing 36(5), 678–701 (2023). https://doi.org/10.1080/0951192X.2022.2128219
[16] Antosz, K., Kulisz, M., Michaluk, J., Knapčíková, L.: Assessment and applicability of machine learning models for quality monitoring in electric vehicle connector manufacturing. In: Knapčíková, L., Peraković, D. (eds.) 10th EAI International Conference on Management of Manufacturing Systems, MMS 2025. EAI/Springer Innovations in Communication and Computing. Springer, Cham (2026). https://doi.org/10.1007/978-3-032-16896-2_14
[17] Tercan, H., Meisen, T.: Machine learning and deep learning based predictive quality in manufacturing: a systematic review. Journal of Intelligent Manufacturing 33(7), 1879–1905 (2022). https://doi.org/10.1007/s10845-022-01963-8
[18] Kausik, A.K., Rashid, A.B., Baki, R.F., Maktum, M.M.J.: Machine learning algorithms for manufacturing quality assurance: a systematic review of performance metrics and applications. Array 26, 100393 (2025). https://doi.org/10.1016/j.array.2025.100393
[19] Hornik, K., Stinchcombe, M., White, H.: Multilayer feedforward networks are universal approximators. Neural Networks 2(5), 359–366 (1989). https://doi.org/10.1016/0893-6080(89)90020-8
[20] Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge, MA (2016). https://www.deeplearningbook.org/
[21] Dubey, S.R., Singh, S.K., Chaudhuri, B.B.: Activation functions in deep learning: a comprehensive survey and benchmark. Neurocomputing 503, 92–108 (2022). https://doi.org/10.1016/j.neucom.2022.06.111
[22] Bai, Y.: ReLU-function and derived function review. SHS Web of Conferences 144, 02006 (2022). https://doi.org/10.1051/shsconf/202214402006
[23] de Giorgio, A., Cola, G., Wang, L.: Systematic review of class imbalance problems in manufacturing. Journal of Manufacturing Systems 71, 620–644 (2023). https://doi.org/10.1016/j.jmsy.2023.10.014
[24] Canbek, G., Taşkaya Temizel, T., Sagiroglu, S.: PToPI: a comprehensive review, analysis, and knowledge representation of binary classification performance measures/metrics. SN Computer Science 4(1), 13 (2022). https://doi.org/10.1007/s42979-022-01409-1
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Copyright (c) 2026 Paula Czarnik, Katarzyna Antosz, Jose Mendes Machado
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