Applications of Machine Learning in Industrial Manufacturing
Research Article
Open Access
CC BY

Applications of Machine Learning in Industrial Manufacturing

Shouheng Wu 1*
1 Xi'an Jiaotong-Liverpool University, Suzhou, China, 215000
*Corresponding author: nefertari_6@163.com
Published on 4 July 2025
Journal Cover
ACE Vol.175
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-80590-237-9
ISBN (Online): 978-1-80590-238-6
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Abstract

Machine learning (ML) has become a key driver of innovation in industrial manufacturing, enhancing quality control, predictive maintenance, and process optimization. Manufacturers can achieve improved efficiency, reduced costs, and enhanced operational reliability by leveraging advanced ML algorithms, such as deep learning and traditional models. However, challenges remain in the large-scale deployment of ML, including issues with data privacy, legacy system interoperability, and the need for high-quality datasets. This paper investigates three core research questions: the enhancement of manufacturing processes via ML algorithms, the technical impediments to ML implementation, and the resolution of these challenges through emerging technologies such as digital twins and IoT. The study reveals that ML has significantly improved fault diagnosis, reduced downtime, and optimized energy use. However, it also highlights ongoing concerns around data privacy and system integration. The paper concludes by discussing the potential of future technologies to advance ML adoption in manufacturing further while emphasizing sustainability and innovative manufacturing initiatives.

Keywords:

Machine Learning, Industrial Manufacturing, Predictive Maintenance, Quality Control, Digital Twin

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Wu,S. (2025). Applications of Machine Learning in Industrial Manufacturing. Applied and Computational Engineering,175,1-7.

References

[1]. Zhang, Y. (2020). Improved fault diagnosis method for bearings based on sparse autoencoders. Mechanical Systems and Signal Processing, 144, 106876.

[2]. Rai, R., et al. (2021). Machine learning-driven predictive maintenance in smart factories. Journal of Intelligent Manufacturing, 34(3), 789–802.

[3]. Chai, H. X., Jin, X., Xu, C., & Xia, C. Q. (2023). A review of 5G-enabled machine learning for industrial IoT. Chinese Journal of Automation, 48(3), 257–276.

[4]. Rai, R., et al. (2021). Machine learning in manufacturing and Industry 4.0 applications. International Journal of Production Research, 59(16), 4773–4778.

[5]. Zhang, Y. (2020). Improved fault diagnosis method for bearings based on sparse autoencoders. Mechanical Systems and Signal Processing, 144, 106876.

[6]. Yu, D., & Deng, L. (2011). Deep learning and its applications to signal and information processing. IEEE Signal Processing Magazine, 28(1), 145-154.

[7]. Sun, L., et al. (2018). Enhanced PCA for fault detection in industrial boilers. Energy Conversion and Management, 176, 102-113.

[8]. Demethual, J., et al. (2015). Fault diagnosis in raw material handling systems using feature extraction and clustering. Journal of Process Control, 35, 120-132.

[9]. Duan, Y. J., Lv, Y. S., Zhang, J., et al. (2016). Deep learning for control: The state of the art and prospects. Acta Automatica Sinica, 42(5), 643-654.

[10]. Bunse, K., et al. (2011). Integrating energy efficiency in industrial management systems. Journal of Cleaner Production, 19(6-7), 662-675.

[11]. Li, Y. J., et al. (2021). A fault diagnosis model for coal mine machinery based on machine learning. Energy and Environmental Protection, 43(10), 241-245.

[12]. Shao, H., Jiang, H., Zhang, X., et al. (2015). Rolling bearing fault diagnosis using an optimization deep belief network. Measurement Science and Technology, 26(11), 115002.

[13]. Ren, H., Qu, J. F., Chai, Y., et al. (2017). Research status and challenges of deep learning in fault diagnosis. Control and Decision, 32(8), 1345-1358.

[14]. Zhang, Y. P. (2023). Application of vibration analysis in rotating machinery fault diagnosis. Metallurgy and Materials, 43(1), 71-73.

[15]. Meng, Y., et al. (2021). Fault detection in industrial boilers using hybrid machine learning models. Energy Reports, 7(4), 1123-1135.

[16]. Gao, H. J. (2023). Fault analysis and treatment in chemical electrical systems. Proceedings of the 7th National Conference on Petrochemical Electrical Technology.

[17]. May, G., et al. (2017). Energy management in Industry 4.0: A roadmap. Procedia CIRP, 61, 299-304.

[18]. International Organization for Standardization (ISO). (2018).  ISO 50001: 2018 Energy management systems. Geneva: ISO.

[19]. Rai, R., Tiwari, M. K., Ivanov, D., & Dolgui, A. (2021). Machine learning in manufacturing and Industry 4.0 applications. International Journal of Production Research, 59(16), 4773-4778.

[20]. Zhao, Y. W. (2023). Machine learning algorithms for industrial scenarios. Master’s Thesis, University of Electronic Science and Technology of China.

[21]. Schulze, M., et al. (2016). Energy management in manufacturing: A systematic review. Journal of Manufacturing Systems, 41, 23-37.

[22]. European Commission. (2009). 2020 climate & energy package. Brussels: European Union

References

[1]. Zhang, Y. (2020). Improved fault diagnosis method for bearings based on sparse autoencoders. Mechanical Systems and Signal Processing, 144, 106876.

[2]. Rai, R., et al. (2021). Machine learning-driven predictive maintenance in smart factories. Journal of Intelligent Manufacturing, 34(3), 789–802.

[3]. Chai, H. X., Jin, X., Xu, C., & Xia, C. Q. (2023). A review of 5G-enabled machine learning for industrial IoT. Chinese Journal of Automation, 48(3), 257–276.

[4]. Rai, R., et al. (2021). Machine learning in manufacturing and Industry 4.0 applications. International Journal of Production Research, 59(16), 4773–4778.

[5]. Zhang, Y. (2020). Improved fault diagnosis method for bearings based on sparse autoencoders. Mechanical Systems and Signal Processing, 144, 106876.

[6]. Yu, D., & Deng, L. (2011). Deep learning and its applications to signal and information processing. IEEE Signal Processing Magazine, 28(1), 145-154.

[7]. Sun, L., et al. (2018). Enhanced PCA for fault detection in industrial boilers. Energy Conversion and Management, 176, 102-113.

[8]. Demethual, J., et al. (2015). Fault diagnosis in raw material handling systems using feature extraction and clustering. Journal of Process Control, 35, 120-132.

[9]. Duan, Y. J., Lv, Y. S., Zhang, J., et al. (2016). Deep learning for control: The state of the art and prospects. Acta Automatica Sinica, 42(5), 643-654.

[10]. Bunse, K., et al. (2011). Integrating energy efficiency in industrial management systems. Journal of Cleaner Production, 19(6-7), 662-675.

[11]. Li, Y. J., et al. (2021). A fault diagnosis model for coal mine machinery based on machine learning. Energy and Environmental Protection, 43(10), 241-245.

[12]. Shao, H., Jiang, H., Zhang, X., et al. (2015). Rolling bearing fault diagnosis using an optimization deep belief network. Measurement Science and Technology, 26(11), 115002.

[13]. Ren, H., Qu, J. F., Chai, Y., et al. (2017). Research status and challenges of deep learning in fault diagnosis. Control and Decision, 32(8), 1345-1358.

[14]. Zhang, Y. P. (2023). Application of vibration analysis in rotating machinery fault diagnosis. Metallurgy and Materials, 43(1), 71-73.

[15]. Meng, Y., et al. (2021). Fault detection in industrial boilers using hybrid machine learning models. Energy Reports, 7(4), 1123-1135.

[16]. Gao, H. J. (2023). Fault analysis and treatment in chemical electrical systems. Proceedings of the 7th National Conference on Petrochemical Electrical Technology.

[17]. May, G., et al. (2017). Energy management in Industry 4.0: A roadmap. Procedia CIRP, 61, 299-304.

[18]. International Organization for Standardization (ISO). (2018).  ISO 50001: 2018 Energy management systems. Geneva: ISO.

[19]. Rai, R., Tiwari, M. K., Ivanov, D., & Dolgui, A. (2021). Machine learning in manufacturing and Industry 4.0 applications. International Journal of Production Research, 59(16), 4773-4778.

[20]. Zhao, Y. W. (2023). Machine learning algorithms for industrial scenarios. Master’s Thesis, University of Electronic Science and Technology of China.

[21]. Schulze, M., et al. (2016). Energy management in manufacturing: A systematic review. Journal of Manufacturing Systems, 41, 23-37.

[22]. European Commission. (2009). 2020 climate & energy package. Brussels: European Union

Cite this article

Wu,S. (2025). Applications of Machine Learning in Industrial Manufacturing. Applied and Computational Engineering,175,1-7.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-CDS 2025 Symposium: Application of Machine Learning in Engineering

ISBN: 978-1-80590-237-9(Print) / 978-1-80590-238-6(Online)
Editor: Marwan Omar, Mian Umer Shafiq
Conference website: https://www.confcds.org
Conference date: 19 August 2025
Series: Applied and Computational Engineering
Volume number: Vol.175
ISSN: 2755-2721(Print) / 2755-273X(Online)

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