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Performance Evaluation and Improvement of Chipset Assembly & Test Production Line Based on Variability

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Abstract

“Factory physics principles” provided a method to evaluate the performance of a simple production line, whose fundamental parameters are known or given. However, it is difficult to obtain the exact and reasonable parameters in actual manufacturing environment, especially for the complex chipset assembly & test production line (CATPL). Besides, research in this field tends to focus on evaluation and improvement of CATPL without considering performance interval and status with variability level. A developed internal benchmark method is proposed, which established three-parameter method based on the Little′s law. It integrates the variability factors, such as processing time, random failure time, and random repair time, to meet performance evaluation and improvement. A case study in a chipset assembly and test factory for the performance of CATPL is implemented. The results demonstrate the potential of the proposed method to meet performance evaluation and emphasise its relevance for practical applications.

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Acknowledgements

The authors are thankful to the anonymous reviewers for their constructive and helpful comments that have led to this much improved manuscript. This work was supported by National Natural Science Foundation of China (No. 71671026) and Sichuan Science and Technology Program (Nos. 2018GZ0306 and 2017GZ0034).

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Authors and Affiliations

  1. School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Chang-Jun Li, Zong-Shi Xie, Xin-Ran Peng & Bo Li

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  1. Chang-Jun Li
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  2. Zong-Shi Xie
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  3. Xin-Ran Peng
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  4. Bo Li
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Correspondence to Bo Li.

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Recommended by Associate Editor Mohammed Chadli

Chang-Jun Li received the B. Sc. degree in administration management from Xichang College, and in computer science and technology from University of Electronic Science and Technology of China, China in 2012, respectively. He is currently a Ph. D. degree candidate in guidance, navigation and control (GNC) at University of Electronic Science and Technology of China, China.

His research interests include production planning and control, fault prediction and diagnosis and maintenance, system integration and automation, computer simulation.

Zong-Shi Xie received the B. Sc. degree in mechanical engineering from University of Electronic Science and Technology of China, China in 2016. Currently, he is a master student in systems engineering at School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, China.

His research interests include mechanical manufacturing and automation, production planning and control.

Xin-Ran Peng received the B. Sc. degree in industrial engineering from Guizhou University, China in 2010, and the M. Sc. degree in pattern recognition and intelligent system at School of Aeronautics and Astronautics, University of Electronic Science and Technology of China (UESTC), China in 2013. She currently works for Bank of China Guizhou branch and engages in project management.

Her research interests include mechanical manufacturing and automation, production planning and control, computer simulation.

Bo Li received the B. Sc. degree in mechanical engineering from the Nanchang Institute of Aeronautic Technology, China in 1997, the M. Sc. degree in mechanical engineering from Guizhou University of Technology, China in 2000, and the Ph. D. degree in mechanical engineering from Zhejiang University, China in 2003. He is now a professor and doctoral supervisor in University of Electronic Science and Technology of China (UESTC), China. He has published about 30 refereed SCI/EI/ISTP journal and conference papers. He is the director of the Intel-UESTC joint lab for advance semiconductor manufacturing and industrial engineering.

His research interests include production planning and control, fault prediction and diagnosis and maintenance, system integration and automation.

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Li, CJ., Xie, ZS., Peng, XR. et al. Performance Evaluation and Improvement of Chipset Assembly & Test Production Line Based on Variability. Int. J. Autom. Comput. 16, 186–198 (2019). https://doi.org/10.1007/s11633-018-1129-8

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