Machine Learning and Artificial Intelligence Supported Machining: A Review and Insights for Future Research

被引：0
作者：
Manikanta, Javvadi Eswara [1 ]
Ambhore, Nitin [4 ]
Dhumal, Amol [2 ]
Gurajala, Naveen Kumar [3 ]
Narkhede, Ganesh [2 ]
机构：
[1] Department of Mechanical Engineering, Shri Vishnu Engineering College for Women, Bhimavaram
[2] Department of Mechanical Engineering, Vishwakarma Institute of Information Technology, Pune
[3] Department of Mechanical Engineering, CMR College of Engineering & Technology, Telangana, Hyderabad
[4] Department of Mechanical Engineering, Vishwakarma Institute of Technology, Pune
来源：
Journal of The Institution of Engineers (India): Series C | 2024年 / 105卷 / 06期
关键词：
Artificial intelligence; Industry; 4.0; and; 5.0; Machine learning; Machining operations;
D O I：
10.1007/s40032-024-01118-z
中图分类号：
TG [金属学与金属工艺]; TH [机械、仪表工业];
学科分类号：
0802 ; 0805 ;
摘要：
Industry 4.0 and 5.0 have led to the extensive implementation of Artificial Intelligence (AI) and Machine Learning (ML). AI and ML signify a significant breakthrough in numerous fields by enabling more efficient data processing, offering enhancements across various services, and automation to replicate the learning process of machines, thereby enhancing system accuracy. In machining processes, AI and ML play crucial roles in predicting cutting forces, tool wear, and optimizing machining parameters. By employing advanced ML systems, machining operations can achieve longer cutting tool lifespan and increased efficiency. Additionally, these systems enable the prediction and enhancement of surface quality in machined components, contributing to overall part quality improvement. Furthermore, ML techniques are instrumental in analyzing and reducing power consumption during machining operations by predicting the energy consumption patterns of machine tools. This paper reviews the applications of AI and ML in machining operations and suggests future research directions. By examining recent achievements in the available literature, it aims to advance the research field by offering innovative concepts and approaches for integrating AI and ML into machining industries. © The Institution of Engineers (India) 2024.
引用
收藏
页码：1653 / 1663
页数：10
相关论文
共 91 条
  • [1] Ordek B., Borgianni Y., Coatanea E., Machine learning-supported manufacturing: A review and directions for future research, Prod. Manuf. Res., 12, 1, (2024)
  • [2] Khan P.W., Kim Y., Byun Y.C., Lee S.J., Influencing factors evaluation of machine learning-based energy consumption prediction, Energies, 14, (2021)
  • [3] Soori M., Arezoo B., Dastres R., Machine learning and artificial intelligence in CNC machine tools, a review, Sustainable Manuf. Service Econ, 2, (2023)
  • [4] Cao J., Xia X., Wang L., Zhang Z., Liu X., A novel CNC milling energy consumption prediction method based on program parsing and parallel neural network, Sustainability, 13, (2021)
  • [5] He Y., Wu P., Li Y., Wang Y., Tao F., Wang Y., A generic energy prediction model of machine tools using deep learning algorithms, Appl. Energy, 275, (2020)
  • [6] Manikanta J.E., Raju B.N., Prasad C., Sankar B.S.S.P., Machining performance on SS304 using nontoxic, biodegradable vegetable-based cutting fluids, Chem. Data Collections, 42, (2022)
  • [7] Liu Z., Guo Y., A hybrid approach to integrate machine learning and process mechanics for the prediction of specific cutting energy, CIRP Ann, 67, pp. 57-60, (2018)
  • [8] Fertig A., Weigold M., Chen Y., (2022)
  • [9] Ullrich K., von Elling M., Gutzeit K., Dix M., Weigold M., Aurich J.C., Wertheim R., Jawahir I.S., Ghadbeigi H., AI-based optimisation of total machining performance: a review, CIRP J. Manufact. Sci. Technol, 50, pp. 40-54, (2024)
  • [10] Plathottam S.J., Rzonca A., Lakhnori R., Iloeje C.O., A review of artificial intelligence applications in manufacturing operations, J. Adv. Manuf. Process, 5, 3, (2023)
12345678910