Data for Process-aware part retrieval for cyber manufacturing using unsupervised deep learning
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Author(s)
Yan, Xiaoliang
Wang, Zhichao
Bjorni, Jacob
Zhao, Changxuan
Dinar, Mahmoud
Rosen, David
Melkote, Shreyes
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Abstract
Cyber manufacturing service, which connects end users with manufacturers over the internet, is significantly hampered by the lack of an automated part retrieval method. The state-of-the-art is focused on automatic shape retrieval, which does not consider manufacturing process requirements, such as material properties. This paper proposes a manufacturing process-aware part retrieval method using deep unsupervised learning that considers both part shape and material properties. Part retrieval results show that the proposed method yields 93.0% process and function class label matching precision, which outperforms the shape-only part retrieval model and supervised learning models trained with process, function, or both labels.
Sponsor
National Science Foundation Award NSF 2113672
Date
2023-07
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