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

Y.-C. Chen, B.-H. He, S.-S. Lin, J.H. Soeseno, Daniel Stanley Tan, T.P.-C. Chen, W.-C. Chen*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


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.
Original languageEnglish
Article numbere4
Number of pages15
JournalAPSIPA Transactions on Signal and Information Processing
Issue number1
Publication statusPublished - Mar 2021
Externally publishedYes


  • Defect detection
  • Functional testing
  • Order forecast
  • Smart manufacturing


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