{"id":4046,"date":"2022-07-15T16:31:49","date_gmt":"2022-07-15T08:31:49","guid":{"rendered":"https:\/\/inventec2.mjitec.tw\/?page_id=4046"},"modified":"2023-07-18T14:07:25","modified_gmt":"2023-07-18T06:07:25","slug":"demystifying-data-and-ai-for-manufacturing","status":"publish","type":"page","link":"https:\/\/inventec2.mjitec.tw\/zh-hans\/ai\/demystifying-data-and-ai-for-manufacturing\/","title":{"rendered":"Demystifying Data and AI for Manufacturing"},"content":{"rendered":"

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Demystifying data and AI for manufacturing: case studies from a major computer maker <\/h2>\n\t <\/div><\/div>[vc_column_text css=”.vc_custom_1660543502950{margin-bottom: 20px !important;}”]<\/p>\n
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APSIPA Transactions on Signal and Information Processing<\/p>\n<\/div>\n

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Yi-Chun Chen, Bo-Huei He, Shih-Sung Lin, Jonathan Hans Soeseno, Daniel Stanley Tan, Trista Pei-Chun Chen and
\nWei-Chao Chen<\/p>\n<\/div>\n

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Mar-21<\/p>\n<\/div>\n

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\u6982\u8981 <\/h2>\n\t <\/div><\/div>[\/vc_column_inner][vc_column_inner el_class=”m_p” width=”2\/3″][vc_column_text]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.<\/p>\n

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.[\/vc_column_text][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row][vc_row][vc_column]

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