APSIPA Transactions on Signal and Information Processing > Vol 10 > Issue 1

Demystifying data and AI for manufacturing: case studies from a major computer maker

Industrial Technology Advances

Yi-Chun Chen, AI Center, Taiwan, Bo-Huei He, Skywatch Innovation Inc., Taiwan, Shih-Sung Lin, Skywatch Innovation Inc., Taiwan, Jonathan Hans Soeseno, AI Center, Taiwan, Daniel Stanley Tan, AI Center, Taiwan, Trista Pei-Chun Chen, AI Center, Taiwan, Wei-Chao Chen, AI Center, Taiwan, chen.wei-chao@inventec.com
 
Suggested Citation
Yi-Chun Chen, Bo-Huei He, Shih-Sung Lin, Jonathan Hans Soeseno, Daniel Stanley Tan, Trista Pei-Chun Chen and Wei-Chao Chen (2021), "Demystifying data and AI for manufacturing: case studies from a major computer maker", APSIPA Transactions on Signal and Information Processing: Vol. 10: No. 1, e4. http://dx.doi.org/10.1017/ATSIP.2021.3

Publication Date: 08 Mar 2021
© 2021 Yi-Chun Chen, Bo-Huei He, Shih-Sung Lin, Jonathan Hans Soeseno, Daniel Stanley Tan, Trista Pei-Chun Chen and Wei-Chao Chen
 
Subjects
 
Keywords
Smart manufacturingOrder forecastFunctional testingDefect detection
 

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This is published under the terms of the Creative Commons Attribution licence.

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In this article:
I. INTRODUCTION 
II. ENABLING SMART MANUFACTURING PROJECTS 
III. LOGISTIC MANAGEMENT 
IV. FUNCTIONAL VERIFICATION 
V. APPEARANCE INSPECTION 
VI. CONCLUSION 

Abstract

In this article, we discuss the backgrounds and technical details about several smart manufacturing projects in a tier-one electronics manufacturing facility. We devise a process to manage logistic forecast and inventory preparation for electronic parts using historical data and a recurrent neural network to achieve significant improvement over current methods. We present a system for automatically qualifying laptop software for mass production through computer vision and automation technology. The result is a reliable system that can save hundreds of man-years in the qualification process. Finally, we create a deep learning-based algorithm for visual inspection of product appearances, which requires significantly less defect training data compared to traditional approaches. For production needs, we design an automatic optical inspection machine suitable for our algorithm and process. We also discuss the issues for data collection and enabling smart manufacturing projects in a factory setting, where the projects operate on a delicate balance between process innovations and cost-saving measures.

DOI:10.1017/ATSIP.2021.3