Overview of machine learning applications in manufacturing: From specialization to transfer learning
DOI:
https://doi.org/10.61591/jslhu.20.644Từ khóa:
Machine learning; Manufacturing functions; Artificial intelligence; Smart manufacturingTóm tắt
The shift of manufacturing systems toward the paradigms of Industry 4.0 and 5.0 has significantly boosted the integration of Machine Learning (ML) technologies in this field. With the rapid growth of research leveraging ML to enhance manufacturing functions, this review aims to provide a thorough and up-to-date overview of these applications. A total of 114 journal articles were collected, analyzed, and categorized based on supervision approaches, ML algorithms, and application domains. The study highlights the benefits of ML in manufacturing, alongside identifying potential avenues for future research. Notably, the paper highlights that the prevalent focus on highly specialized applications in manufacturing could be mitigated by encouraging the implementation of transfer learning within the industry.
Tài liệu tham khảo
Kusiak, A. “Smart manufacturing”. International Journal of Production Research, 56(1–2), pp. 508–517, 2017.
Mittal, S., et al., “Smart manufacturing: Characteristics and technologies”. IFIP Advances in Information and Communication Technology, 492, pp. 539–548, 2016.
Chang, T.-W., et al., “Predicting magnetic characteristics of additive manufactured soft magnetic composites by machine learning,” The International Journal of Advanced Manufacturing Technology, 114(10), pp. 3177–3184, 2021.
Long, J., et al., “Yield and properties prediction based on the multicondition LSTM model for the solvent deasphalting process,” American Chemical Society Omega, 8(6), pp. 5437–5450, 2023.
Hoefer, M. J., & Frank, M. C. “Automated manufacturing process selection during conceptual design,” Journal of Mechanical Design, Transactions of the ASME, 140(3), 2018.
Papananias, M., et al., “Inspection by exception: A new machine learning-based approach for multistage manufacturing,” Applied Soft Computing, 97, 106787, 2020.
Yu, T., Huang, J., & Chang, Q. “Mastering the working sequence in human-robot collaborative assembly based on reinforcement learning,” IEEE Access, 8, pp. 163868–163877, 2020.
Dogan, A., and Birant, D. “Machine learning and data mining in manufacturing,” Expert Systems with Applications, 166, 114060, 2021.
Kahng, H., and Kim, S. B. “Self-supervised representation learning for wafer bin map defect pattern classification,” IEEE Transactions on Semiconductor Manufacturing, 34(1), pp. 74–86, 2021.
Kang, Z., Catal, C., and Tekinerdogan, B. “Machine learning applications in production lines: A systematic literature review,” Computers & Industrial Engineering, 149, 106773, 2020.
Srinivasan, S. et al., “Laser powder bed fusion parameter selection via machine-learning-augmented process modeling,” JOM, 72(12), pp. 4393–4403, 2020.
Kononenko, I., & Kukar, M. “Machine learning and data mining,” In Machine learning and data mining, pp. 1–36, 2007.
Reinders, C. et al., “Learning convolutional neural networks for object detection with very little training data,” In M. Y. Y. Yang, B. Rosenhahn, & V. Murino (Eds.), Multimodal scene understanding: Algorithms, applications and Deep Learning, pp. 65–100, 2019.
C. Zhang et al., "Deep learning-based predictive analytics for smart manufacturing: A review," Journal of Manufacturing Systems, 2022.
T. A. Nguyen et al., "AI-driven data analytics in manufacturing: Recent trends and applications," Advanced Engineering Informatics, 2023.
H. Lee et al., "Machine learning applications for predictive maintenance in smart factories," International Journal of Production Research, 2023.
Y. Chen and J. Wu, "Industrial IoT data analytics and ML algorithms: Challenges and opportunities," IEEE Transactions on Industrial Informatics, 2022.
S. Kumar et al., "Dynamic process control in manufacturing using machine learning," Computers & Industrial Engineering, 2023.
F. Rossi et al., "The role of ML in Industry 4.0 for smart and sustainable manufacturing," Journal of Cleaner Production, 2023.
M. Ahmed and A. Hassan, "Advancing towards Industry
0: Integration of sensors and machine learning in manufacturing," Sensors, 2022.
Bertolini, M., Mezzogori, D., Neroni, M., & Zammori, F. (2021). Machine learning for industrial applications: A comprehensive literature review. Expert Systems with Applications, 175, 114820.
DOI: https://doi.org/10.1016/J.ESWA.2021.114820
Dogan, A., & Birant, D. (2021). Machine learning and data mining in manufacturing. Expert Systems with Applications 166, 114060.
DOI: https://doi.org/10.1016/J.ESWA.2020.114060
Barrionuevo, G. O. et al., “Comparative evaluation of supervised machine learning algorithms in the prediction of the relative density of 316L stainless steel fabricated by selective laser melting,” The International Journal of Advanced Manufacturing Technology, 113(1–2), pp. 419–433, 2021.
Wang, B., et al., “A hybrid machine learning approach to determine the optimal processing window in femtosecond laser-induced periodic nanostructures,” Journal of Materials Processing Technology, 308, 117716, 2022.
Mypati, O., et al., “A critical review on applications of artificial intelligence in manufacturing,” Artificial Intelligence Review, 56(S1), pp. 661–768, 2023.
Nti, I. K., et al., “Applications of artificial intelligence in engineering and manufacturing: A systematic revie,” Journal of Intelligent Manufacturing, 33(6), pp. 1581–1601, 2021.
Bertolini, M., et al., “Machine learning for industrial applications: A comprehensive literature review,” Expert Systems with Applications, 175, 114820, 2021.
Li, J., et al., “A flexible manufacturing assembly system with deep reinforcement learning,” Control Engineering Practice, 118, 104957, 2022.
Garouani, M., et al., “Using meta-learning for automated algorithms selection and configuration: An experimental framework for industrial big data,” Journal of Big Data, 9(1), pp. 1–20, 2022.