Research on Typical Application Scenarios and Standards System of Artificial Intelligence in Intelligent Manufacturing

Ruiqi Li, Sha Wei, Yuhang Cheng, Baocui Hou

Strategic Study of CAE ›› 2018, Vol. 20 ›› Issue (4) : 112-117.

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Strategic Study of CAE ›› 2018, Vol. 20 ›› Issue (4) : 112-117. DOI: 10.15302/J-SSCAE-2018.04.018
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Research on Typical Application Scenarios and Standards System of Artificial Intelligence in Intelligent Manufacturing

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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.

Keywords

artificial intelligence / intelligent manufacturing / enterprise’s KPI / standardization

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Ruiqi Li, Sha Wei, Yuhang Cheng, Baocui Hou. Research on Typical Application Scenarios and Standards System of Artificial Intelligence in Intelligent Manufacturing. Strategic Study of CAE, 2018, 20(4): 112‒117 https://doi.org/10.15302/J-SSCAE-2018.04.018

References

[1]
Zhou J, Li P G, Zhou Y H, et al. Toward new-generation intelligent manufacturing [J]. Engineering, 2018, 4(1) : 11–20.
[2]
沈清泓. 企业制造执行系统和关键性能指标评估技术研究 [D]. 杭州: 浙江大学(博士毕业论文), 2013.
[3]
Jovan V, Zorzut S. Use of key performance indicators in produc-tion management [C]. Bangkok : Cybernetics and Intelligent Sys-tems, 2006.
[4]
Godfrey P. Overall equipment effectiveness [J]. Manufacturing Engineer, 2002, 81(3): 109–112.
[5]
Hansen R C. Overall equipment effectiveness: A powerful produc-tion/maintenance tool for increased profits [M]. New York : Indus-trial Press Inc., 2001.
[6]
Rios H, González E, Rodriguez C, et al. A mobile solution to en-hance training and execution of troubleshooting techniques of the engine air bleed system on Boeing 737 [J]. Procedia Computer Science, 2013, 25: 161–170.
[7]
尹旭悦, 范秀敏, 王磊, 等. 航天产品装配作业增强现实引导训练系统及应用 [J]. 航空制造技术, 2018, 61(1/2): 48–53.
[8]
涂忆柳, 李晓东. 维修工程管理研究与发展综述 [J]. 工业工程与管理, 2004, 9(4): 7–12.
[9]
高宏力, 李登万, 许明恒. 基于人工智能的丝杠寿命预测技术 [J]. 西南交通大学学报, 2010, 45(5): 685–691.
[10]
谭辉, 张洪伟, 朱丽. APS 系统中基于改进的遗传算法的分布式排产研究 [J]. 计算机应用研究, 2005, 22(6): 76–79.
[11]
Xu K, Li S, Zheng S. The value and governance of industrial big data [J]. Modern Management, 2017, 7(5): 245–252.
[12]
郑金驹, 李文龙, 王瑜辉, 等. QFP 芯片外观视觉检测系统及检测方法 [J]. 中国机械工程, 2013, 24(3): 290–294, 301.
[13]
中国电子技术标准化研究院.人工智能标准化白皮书 (2018版 ) [R]. 北京 : 中国电子技术标准化研究院, 2018.
Funding
CAE Advisory Project “Research on Intelligent Manufacturing Led by New-Generation Artificial Intelligence” (2017-ZD-08-03)
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