Intelligent Machining Technology in Cutting Process

被引：10
|
作者：
Liu X. [1 ]
Liu Q. [1 ]
Yue C. [1 ]
Wang L. [2 ]
Liang S.Y. [3 ]
Ji W. [1 ]
Gao H. [1 ]
机构：
[1] The lab of National and Local United Engineering for High-Efficiency Cutting & Tools, Harbin University of Science and Technology, Harbin
[2] KTH Royal Institute of Technology, Stockholm
[3] George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta
来源：
Jixie Gongcheng Xuebao/Journal of Mechanical Engineering | 2018年 / 54卷 / 16期
关键词：
Artificial intelligence; Cutting process; Intelligent machining technology; On-line monitoring; Optimize control; Simulation and optimization;
D O I：
10.3901/JME.2018.16.045
中图分类号：
学科分类号：
摘要：
Metal cutting is a very complex process. In the cutting process, the related knowledge and theories of physics, chemistry, mechanics, materials science, vibration, tribology, heat transfer and other fields are involved. The cutting process control has been the focus of the cutting research. With the development of machining technology and the coming of the Industry 4. 0, researchers are getting more concerned with the intelligent machining technology. It is an inevitable trend to apply the intelligent machining technology in the cutting process. The connotation and the application process of intelligent machining technology is expounded to investigate the critical technology in intelligent manufacturing. The research results in the simulation and optimization, cutting process condition monitoring, and optimization control are reviewed. Through analyzing the application prospect and problems of intelligent machining technology, the main scientific problems and key technologies to be solved are proposed. Intelligent machining is the development direction of processing technology. The application of intelligent machining technology in the cutting process will bring another technological revolution in the manufacturing industry. © 2018 Journal of Mechanical Engineering.
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页码：45 / 61
页数：16
相关论文
共 67 条
  • [1] Kang H.S., Ju Y.L., Choi S.S., Et al., Smart manufa-cturing: Past research, present findings, and future directions, International Journal of Precision Engineering and Manufacturing-Green Technology, 3, 1, pp. 111-128, (2016)
  • [2] Liang S.Y., Hecker R.L., Landers R.G., Machining process monitoring and control: The state-of-the-art, Journal of Manufacturing Science & Engineering, 126, 2, pp. 599-610, (2004)
  • [3] Zhang J., Liang S.Y., Yao J., Et al., Evolutionary optimization of machining processes, Journal of Intelligent Manufacturing, 17, 2, pp. 203-215, (2006)
  • [4] Davis J., Edgar T., Porter J., Et al., Smart manufacturing, manufacturing intelligence and demand-dynamic performance, Computers & Chemical Engineering, 47, 12, pp. 145-156, (2012)
  • [5] Wang L., Torngren M., Onori M., Current status and advancement of cyber-physical systems in manufacturing, Journal of Manufacturing Systems, 37, pp. 517-527, (2015)
  • [6] Wang L., An overview of function block enabled adaptive process planning for machining, Journal of Manufacturing Systems, 35, pp. 10-25, (2015)
  • [7] Wang L., Machine availability monitoring and machining process planning towards Cloud manufacturing, Cirp Journal of Manufacturing Science & Technology, 6, 4, pp. 263-273, (2013)
  • [8] Wang Z., Jiao L., Yan P., Et al., Research and development of intelligent cutting database cloud platform system, International Journal of Advanced Manufacturing Technology, pp. 1-13, (2016)
  • [9] Cai L., Tian Y., Liu Z., Et al., Application of cloud computing to simulation of a heavy-duty machine tool, International Journal of Advanced Manufacturing Technology, 84, 1, pp. 291-303, (2015)
  • [10] Wu M., Huo T., Ge J., Cutting process-based optimization model of machining feature for cloud manufacturing, International Journal of Advanced Manufacturing Technology, 84, 1, pp. 327-334, (2016)
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