Development of Advanced Manufacturing Cloud of Things (AMCoT)—A Smart Manufacturing Platform
| Autor: | Haw-Ching Yang, Min-Hsiung Hung, Chao-Chun Chen, Yu-Chuan Lin, Yao-Sheng Hsieh, Hsien-Cheng Huang, Fan-Tien Cheng |
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| Rok vydání: | 2017 |
| Předmět: |
0209 industrial biotechnology
Engineering Control and Optimization business.industry Mechanical Engineering Integrated Computer-Aided Manufacturing Big data Biomedical Engineering Cloud computing 02 engineering and technology Manufacturing engineering Predictive maintenance Computer Science Applications Human-Computer Interaction 020901 industrial engineering & automation Computer-integrated manufacturing Artificial Intelligence Control and Systems Engineering Manufacturing Process development execution system 0202 electrical engineering electronic engineering information engineering Advanced manufacturing 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition business |
| Zdroj: | IEEE Robotics and Automation Letters. 2:1809-1816 |
| ISSN: | 2377-3774 |
| Popis: | As semiconductor manufacturing processes are becoming more and more sophisticated, how to maintain their feasible production yield becomes an important issue. Also, how to build a smart manufacturing platform that can facilitate realizing smart factories is essential and desirable for current manufacturing industries. Aimed at addressing the above-mentioned two issues, in this letter, a five-stage approach for enhancing and assuring yield is proposed. Also, a smart manufacturing platform- Advanced Manufacturing Cloud of Things (AMCoT) based on Internet of Things, cloud computing, big data analytics, cyber-physical systems, and prediction technologies is designed and implemented to realize the proposed five-stage approach of yield enhancement and assurance. Finally, AMCoT is applied to a bumping process of a semiconductor company in Taiwan to conduct industrial case studies. Testing results demonstrate that AMCoT possesses capabilities of conducting total inspection in production, providing prognosis, and predictive maintenance on equipment, finding the root cause of yield loss, and storing and handling big production data, which as a whole is promising to achieve the goal of zero defects. |
| Databáze: | OpenAIRE |
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