Making costly manufacturing smart with transfer learning under limited data: A case study on composites autoclave processing |
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| Affiliation: | 1. Materials and Manufacturing Research Institute, University of British Columbia, Kelowna, V1V 1V7, Canada;2. Department of Computer Science, University of British Columbia, Kelowna, V1V 1V7, Canada;3. Institute of Structures and Design, German Aerospace Center (DLR), Stuttgart, Pfaffenwaldring 38-40, D-70569, Germany;1. College of Business Administration, Ewha Womans University, 52 Ewhayeodae-gil, Seodaemun-gu, Seoul, Republic of Korea;2. Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, USA;1. Shenzhen Research Institute, State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;2. State Key Laboratory of Heavy Duty AC Drive Electric Locomotive Systems Integration, Zhuzhou, Hunan 412001, China;3. Department of Production Engineering, KTH Royal Institute of Technology, Stockholm 10044, Sweden;1. Department of Computer Science, Vanderbilt University, Nashville, TN 37212, United States;2. Department of Mechanical Engineering, Virginia Tech, Blacksburg, VA 24060, United States;1. Department of Mechanical Engineering, University of Michigan, 2350 Hayward St, Ann Arbor, MI 48109, United States;2. Department of Aerospace Engineering, University of Michigan, 1320 Beal Ave, Ann Arbor, MI, 48109, United States |
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| Abstract: | The integration of advanced manufacturing processes with ground-breaking Artificial Intelligence methods continue to provide unprecedented opportunities towards modern cyber-physical manufacturing processes, known as smart manufacturing or Industry 4.0. However, the “smartness” level of such approaches closely depends on the degree to which the implemented predictive models can handle uncertainties and production data shifts in the factory over time. In the case of change in a manufacturing process configuration with no sufficient new data, conventional Machine Learning (ML) models often tend to perform poorly. In this article, a transfer learning (TL) framework is proposed to tackle the aforementioned issue in modeling smart manufacturing. Namely, the proposed TL framework is able to adapt to probable shifts in the production process design and deliver accurate predictions without the need to re-train the model. Armed with sequential unfreezing and early stopping methods, the model demonstrated the ability to avoid catastrophic forgetting in the presence of severely limited data. Through the exemplified industry-focused case study on autoclave composite processing, the model yielded a drastic (88%) improvement in the generalization accuracy compared to the conventional learning, while reducing the computational and temporal cost by 56%. |
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| Keywords: | Intelligent manufacturing Transfer learning Limited data Autoclave processing Aerospace composites |
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