2018, Volume 20, Issue 4
Strategic Study of CAE >> 2018, Volume 20, Issue 4 doi: 10.15302/J-SSCAE-2018.04.018
Research on Typical Application Scenarios and Standards System of Artificial Intelligence in Intelligent Manufacturing
1. China Electronics Standardization Institute, Beijing 100007, China;
2. Beijing Aerospace Intelligent Manufacturing Technology Development Co., Ltd., Beijing 100039, China
Next Previous
Abstract
In terms of the artificial intelligence (AI) application in intelligent manufacturing, this paper analyzes the system realization form of intelligent manufacturing based on the definition of enterprise's key performance indicators (KPI), and further discusses the main role of AI in intelligent manufacturing. Based on the typical application scenarios of AI in intelligent manufacturing, this paper puts forward the application map of AI in intelligent manufacturing from the life cycle dimension, summarizes the common technologies in AI application to intelligent manufacturing, and illustrates the influence of AI on enterprises by taking production as an example. Finally, this paper puts forward the standards system of AI in intelligent manufacturing.
Figures
Fig.1
Fig.2
Fig.3
Fig.4
References
[ 1 ] Zhou J, Li P G, Zhou Y H, et al. Toward new-generation intelligent manufacturing [J]. Engineering, 2018, 4(1): 11–20. link1 link2
[ 2 ] Shen Q H. Research on manufacturing execution system and performance index indicator evaluation technology of enterprise [D]. Hangzhou: Zhejiang University (Doctoral dissertation), 2013. Chinese. link1 link2
[ 3 ] Jovan V, Zorzut S. Use of key performance indicators in production management [C]. Bangkok: Cybernetics and Intelligent Systems, 2006.
[ 4 ] Godfrey P. Overall equipment effectiveness [J]. Manufacturing Engineer, 2002, 81(3): 109–112.
[ 5 ] Hansen R C. Overall equipment effectiveness: A powerful production/maintenance tool for increased profits [M]. New York: Industrial Press Inc., 2001. link1 link2
[ 6 ] Rios H, González E, Rodriguez C, et al. A mobile solution to enhance training and execution of troubleshooting techniques of the engine air bleed system on Boeing 737 [J]. Procedia Computer Science, 2013, 25: 161–170. link1 link2
[ 7 ] Yin X Y, Fan X M, Wang L, et al. Augmented reality training system for aerospace product assembly process guidance and its application [J]. Aeronautical Manufacturing Technology, 2018, 61(1/2): 48–53. Chinese. link1 link2
[ 8 ] Tu Y L, Li X D. Review on the research and development of maintenance engineering management [J]. Industrial Engineering and Management, 2004, 9(4): 7–12. Chinese. link1 link2
[ 9 ] Gao H L, Li D W, Xu M H. Intelligent monitoring system for screw life evaluation [J]. Journal of Southwest Jiaotong University, 2010, 45(5): 685–691. Chinese. link1 link2
[10] Tan H, Zhang H W, Zhu L. Research of distributed scheduling based on modified GA in APS [J]. Application Research of Computers, 2005, 22(6): 76–79. Chinese. link1 link2
[11] Xu K, Li S, Zheng S. The value and governance of industrial big data [J]. Modern Management, 2017, 7(5): 245–252.
[12] Zheng J, Li W L, Wang Y H, et al. QFP chip visual inspection system and its inspection method [J]. China Mechanical Engineering, 2013, 24(3): 290–294, 301. Chinese. link1 link2
[13] Ministry of Industry and Information Technology, Standardization Administration of China. National Intelligent Manufacturing Standards System Construction Guideline [R]. Beijing: Ministry of Industry and Information Technology, Standardization Administration of China, 2018. Chinese.
[14] China Electronics Standardization Institute. White paper on artificial intelligence standardization (2018) [R]. Beijing: China Electronics Standardization Institute, 2018. Chinese
京公网安备 11010502051620号
