走向智能机床

Jihong Chen, Pengcheng Hu, Huicheng Zhou, Jianzhong Yang, Jiejun Xie, Yakun Jiang, Zhiqiang Gao, Chenglei Zhang

工程(英文) ›› 2019, Vol. 5 ›› Issue (4) : 679-690.

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PDF(3895 KB)
工程(英文) ›› 2019, Vol. 5 ›› Issue (4) : 679-690. DOI: 10.1016/j.eng.2019.07.018
研究论文
RESEARCH ARTICLE

走向智能机床

作者信息 +

Toward Intelligent Machine Tool

Author information +
History +

摘要

随着现代信息技术特别是新一代人工智能技术的发展,智能机床技术迎来了新的发展机遇。本文在中国工程院定义的智能制造三个范式的基础上,系统地阐述了智能机床的概念、内涵、特征和体系架构。揭示了机床从手动机床演化到智能机床的三个阶段(数控机床、互联网+ 机床以及智能机床),详细分析了智能机床的自主感知与连接、自主学习与建模、自主优化与决策和自主控制与执行这四项智能化控制的实现原理,提出了智能机床通过数据学习形成并积累知识的本质特征,独创发明了指令域分析方法、物理和大数据混合建模技术、i 代码和双码联控等关键使能技术。基于以上研究,研制了智能数控系统和智能机床工业样机。通过基于Cyber NC 和双码联控的加工质量优化、基于大数据建模的工艺参数优化和基于深度学习的机床进给系统建模及误差补偿这三个智能化技术应用案例的实践,验证了新一代人工智能技术与制造技术的深度融合,是机床实现从“互联网+ 机床”向“智能+ 机床”演化的便捷有效途径。

Abstract

With the development of modern information technology—and particularly of the new generation of artificial intelligence (AI) technology—new opportunities are available for the development of the intelligent machine tool (IMT). Based on the three classical paradigms of intelligent manufacturing as defined by the Chinese Academy of Engineering, the concept, characteristics, and systemic structure of the IMT are presented in this paper. Three stages of machine tool evolution—from the manually operated machine tool (MOMT) to the IMT—are discussed, including the numerical control machine tool (NCMT), the smart machine tool (SMT), and the IMT. Furthermore, the four intelligent control principles of the IMT—namely, autonomous sensing and connection, autonomous learning and modeling, autonomous optimization and decision-making, and autonomous control and execution—are presented in detail. This paper then points out that the essential characteristic of the IMT is to acquire and accumulate knowledge through learning, and presents original key enabling technologies, including the instruction-domain-based analytical approach, theoretical and big-data-based hybrid modeling technology, and the double-code control method. Based on this research, an intelligent numerical control (INC) system and industrial prototypes of IMTs are developed. Three intelligent practices are conducted, demonstrating that the integration of the new generation of AI technology with advanced manufacturing technology is a feasible and convenient way to advance machine tools toward the IMT.

关键词

智能制造 / 智能机床 / 智能数控系统 / 新一代人工智能

Keywords

Intelligent manufacturing / Intelligent machine tool / Intelligent numerical controller / New-generation artificial intelligence

引用本文

导出引用
Jihong Chen, Pengcheng Hu, Huicheng Zhou. 走向智能机床. Engineering. 2019, 5(4): 679-690 https://doi.org/10.1016/j.eng.2019.07.018

参考文献

[1]
Zhou J, Li P, Zhou Y, Wang B, Zang J, Meng L. Toward new-generation intelligent manufacturing. Engineering 2018;4(1):11–20.
[2]
Zhou J. Intelligent manufacturing—main direction of ‘‘Made in China 2025”. China Mech Eng 2015;26(17):2273–84.
[3]
Chen J, Yang J, Zhou H, Xiang H, Zhu Z, Li Y, et al. CPS modeling of CNC machine tool work processes using an instruction-domain based approach. Engineering 2015;1(2):247–60.
[4]
Liu RL, Zhang CR, Jiang Y, Wang K. Networked monitoring technology of numerical control machine tools based on MTConnect. Comput Integr Manuf Syst 2013;19(5):1078–84.
[5]
Rehorn AG, Sejdic´ E, Jiang J. Fault diagnosis in machine tools using selective regional correlation. Mech Syst Signal Process 2006;20(5):1221–38.
[6]
Kim DH, Song JY, Cha SK, Son H. The development of embedded device to detect chatter vibration in machine tools and CNC-based autonomous compensation. J Mech Sci Technol 2011;25(10):2623.
[7]
Zhang S. Smart manufacturing and i5 smart machine tools. Mach Build Autom 2017;46(1):1–8. Chinese.
[8]
Vijayaraghavan A, Sobel W, Fox A, Dornfeld D, Warndorf P. Improving machine tool interoperability using standardized interface protocols: MTConnect. In: Proceedings of 2008 International Symposium on Flexible Automation; 2008 Jun 23–26; Atlanta, GA, USA; 2008.
[9]
Hu L, Nguyen NT, Tao W, Leu MC, Liu XF, Shahriar MR, et al. Modeling of cloudbased digital twins for smart manufacturing with MTConnect. Procedia Manuf 2018;26:1193–203.
[10]
Umati: universal machine tool interface [Internet]. Frankfort: German Machine Tool Builders’ Association; [cited 2019 Jun 17]. Available from: https://vdw.de/en/technology-and-standardisation/umati-universal-machinetool-interface/.
[11]
Weber A. GE ‘‘predix” the future of manufacturing. Assembly 2017;60(3): GE70–6.
[12]
Siemens y TCS unen fuerzas para impulsar el IoT industrial en MindSphere. Eurofach Electron Actual Tecnol Ind Electrón 2017;(459):28–9. Spanish.
[13]
Zhou H, Zhang C, Jiang Y, Chen J, inventors; Huazhong University of Science and Technology, assignee. [Double-code based control method of NC machining and the corresponding device]. China Patent CN201810305822.9. 2018 Nov 2. Chinese.
[14]
Zhou J, Zhou Y, Wang B, Zang J. Human–cyber–physical systems (HCPSs) in the context of new-generation intelligent manufacturing. Engineering. Forthcoming 2019.
[15]
Zhou H, Lang M, Hu P, Su Z, Chen J. The modeling, analysis, and application of the in-process machining data for CNC machining. Int J Adv Manuf Technol 2019;102(5–8):1051–66.
[16]
Zain AM, Haron H, Sharif S. Application of GA to optimize cutting conditions for minimizing surface roughness in end milling machining process. Expert Syst Appl 2010;37(6):4650–9.
[17]
An LB, Feng LJ, Lu CG. Cutting parameter optimization for multi-pass milling operations by genetic algorithms. Adv Mat Res 2011;160–162:1738–43.
[18]
Saffar RJ, Razfar MR. Simulation of end milling operation for predicting cutting forces to minimize tool deflection by genetic algorithm. Mach Sci Technol 2010;14(1):81–101.
[19]
Zuperl U, Cus F. Tool cutting force modeling in ball-end milling using multilevel perceptron. J Mater Process Technol 2004;153–154:268–75.
[20]
Zuperl U, Cus F, Reibenschuh M. Neural control strategy of constant cutting force system in end milling. Robot Comput-Integr Manuf 2011;27(3):485–93.
[21]
Yeung CH, Altintas Y, Erkorkmaz K. Virtual CNC system. Part I. System architecture. Int J Mach Tools Manuf 2006;46(10):1107–23.
[22]
Yang S, Ghasemi AH, Lu X, Okwudire CE. Pre-compensation of servo contour errors using a model predictive control framework. Int J Mach Tools Manuf 2015;98:50–60.
[23]
Erkorkmaz K, Altintas Y. High speed CNC system design. Part II: modeling and identification of feed drives. Int J Mach Tools Manuf 2001;41(10):1487–509.
[24]
Huo F, Poo AN. Nonlinear autoregressive network with exogenous inputs based contour error reduction in CNC machines. Int J Mach Tools Manuf 2013;67:45–52.
[25]
Li Z, Wang Y, Wang K. A data-driven method based on deep belief networks for backlash error prediction in machining centers. J Intell Manuf. Epub 2017 Dec 19.
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