
以人为本的智能制造:理念、技术与应用
Human-Centered Intelligent Manufacturing: Overview and Perspectives
人是制造生产活动中最具能动性和最具活力的因素,智能制造最终需回归到服务和满足人们美好生活需求上来。本文基于人–信息–物理系统(HCPS)智能制造发展理论,提出以人为本的智能制造(人本智造)的基本概念,并从发展背景、基本内涵、人的因素、技术体系、应用实践等方面对人本智造进行了分析探讨。研究指出,人本智造体现了智能制造发展的一种重要理念,同时也是新一代智能制造系统的一个重要技术方向。在此基础上,针对人本智造从政策、企业、科研3个层面提出了若干建议:及时对接国家相关战略、企业将“以人为本”作为发展智能制造的重要理念、重视智能制造系统中人因工程的研究等,以促进以人为本的智能制造在我国的发展和应用。
Human is the most dynamic factor in a manufacturing system; no matter how advanced intelligent manufacturing would be, it should meet humans’ needs and serve for a better life. Based on the theory of human–cyber–physical systems (HCPS) in the context of intelligent manufacturing, the concept of human-centered intelligent manufacturing (HCIM) is firstly proposed in this work. HCIM is discussed from the aspects of background, connotation, human factors, technical system, and practical applications. It clarifies that HCIM not only reflects an important perspective, but also represents one of the significant research directions of intelligent manufacturing. On this basis, several suggestions are recommended from policy decision-making, enterprise development, and scientific research levels, including linking HCIM with relevant national strategies, regarding HCIM as a key concept for enterprises’development, and enhancing research on human factors/ergonomics in intelligent manufacturing systems. It’s expected that this work can provide a reference to promote HCIM development and applications in China.
以人为本 / 新一代智能制造 / 人– 信息– 物理系统 / 人本智造
human-centered / new-generation intelligent/smart manufacturing / human–cyber–physical systems (HCPS) / humancentered intelligent manufacturing (HCIM)
[1] |
周济. 智能制造—— “中国制造 2025”的主攻方向 [J]. 中国机械 工程, 2015, 26(17): 2273-2284. Zhou J. Intelligent manufacturing―Main direction of “Made in China 2025” [J]. China Mechanical Engineering, 2015, 26(17): 2273-2284.
|
[2] |
Zhou J, Li P G, Zhou Y H, et al. Toward new-generation intelligent manufacturing [J]. Engineering, 2018, 4(1): 11-20.
|
[3] |
Zhou J, Zhou Y H, Wang B C, et al. Human–cyber–physical systems (HCPSs) in the context of new-generation intelligent manufacturing [J]. Engineering, 2019, 5(4): 624-636.
|
[4] |
Wang B C, Hu S J, Sun L, et al. Intelligent welding system technologies: State-of-the-art review and perspectives [J]. Journal of Manufacturing Systems, 2020, 56: 373–391.
|
[5] |
王柏村, 臧冀原, 屈贤明, 等. 基于人–信息–物理系统 (HCPS) 的 新一代智能制造研究 [J]. 中国工程科学, 2018, 20(4): 29-34. Wang B C, Zang J Y, Qu X M, et al. Research on new-generation intelligent manufacturing based on human–cyber–physical systems [J]. Strategic Study of CAE, 2018, 20(4): 29-34.
|
[6] |
李清, 唐骞璘, 陈耀棠, 等. 智能制造体系架构、参考模型与标准 化框架研究 [J]. 计算机集成制造系统, 2018, 24(3): 539-549. Li Q, Tang Q L, Chen Y T, et al. Smart manufacturing standardization: Reference model and standards framework [J]. Computer Integrated Manufacturing Systems, 2018, 24(3): 539-549.
|
[7] |
张伯鹏, 汪劲松. 制造系统中知识信息与人的作用 [J]. 机械工 程学报, 1994, 30 (5): 61-65. Zhang B P, Wang J S. Knowledge information and human function in manufacturing systems [J]. Journal of Mechanical Engineering, 1994, 30(5): 61-65.
|
[8] |
陈国权. 先进制造技术系统研究开发和应用的关键——人的因 素 [J]. 中国机械工程, 1996, 7(1): 12-14. Chen G Q. Human factors—The key to the research, development and application of advanced manufacturing technology system [J]. China Mechanical Engineering, 1996, 7(1): 12-14.
|
[9] |
Nunes D, Sá Silva J, Boavida F. A practical introduction to humanin-the-loop cyber–physical systems [M]. Hoboken: John Wiley & Sons Ltd., 2018.
|
[10] |
Madni A M, Sievers M, Madni C C. Adaptive cyber–physical– human systems: Exploiting cognitive modeling and machine learning in the control loop [J]. Insight, 2018, 21(3): 87-93.
|
[11] |
Madni A M. Exploiting augmented intelligence in systems engineering and engineered systems [J]. Insight, 2020, 23(1): 31- 36.
|
[12] |
Jin M. Data-efficient analytics for optimal human–cyber–physical systems [D]. Berkeley: University of California, Berkeley(Doctoral dissertation), 2017.
|
[13] |
Romero D, Bernus P, Noran O, et al. The operator 4.0: Human– cyber–physical systems & adaptive automation towards human-automation symbiosis work systems [C]. Iguassu Falls: International Conference on Advances in Production Management Systems, 2016.
|
[14] |
Ruppert T, Jaskó S, Holczinger T, et al. Enabling technologies for operator 4.0: A survey [J]. Applied Sciences, 2018, 8(9): 1-19.
|
[15] |
孙林岩. 人因工程 [M]. 北京: 科学出版社, 2011. Sun L Y. Human factors engineering [M]. Beijing: China Science Publishing & Media Ltd., 2011.
|
[16] |
Dannapfel M, Burggräf P, Bertram S, et al. Systematic planning approach for heavy-duty human–robot cooperation in automotive flow assembly [J]. International Journal of Electrical and Electronic Engineering and Telecommunications, 2018, 7: 51-57.
|
[17] |
Ma M, Lin W, Pan D, et al. Data and decision intelligence for human-in-the-loop cyber–physical systems: Reference model, recent progresses and challenges [J]. Journal of Signal Processing Systems, 2017, 90(8): 1167-1178.
|
[18] |
Fantini P, Pinzone M, Taisch M. Placing the operator at the centre of Industry 4.0 design: Modelling and assessing human activities within cyber–physical systems [J]. Computers & Industrial Engineering, 2018, 139: 1-11.
|
[19] |
Pacaux-Lemoine M P, Trentesaux D, Zambrano Rey G, et al. Designing intelligent manufacturing systems through human– machine cooperation principles: A human-centered approach [J]. Computers & Industrial Engineering, 2017, 111: 581-595.
|
[20] |
Raina A, McComb C, Cagan J. Learning to design from humans: Imitating human designers through deep learning [J]. Journal of Mechanical Design, 2019, 141(11): 111102.
|
[21] |
Raina A, Cagan J, McComb C. Transferring design strategies from human to computer and across design problems [J]. Journal of Mechanical Design, 2019, 141(11): 114501.
|
[22] |
Wang L, Gao R, Váncza J, et al. Symbiotic human–robot collaborative assembly [J]. CIRP Annals Manufacturing Technology, 2019, 68(2): 701-726.
|
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|
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