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Deep learning and sequence mining for manufacturing process and sequence selection
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Deep learning and sequence mining for manufacturing process and sequence selection

Author

Listed:
  • Changxuan Zhao
  • Mahmoud Dinar
  • Shreyes N. Melkote

Abstract

Automatic determination of manufacturing process sequences for the physical production of given part designs is key to facilitate on-demand cyber manufacturing. In this work, we propose an integrated framework that (i) identifies manufacturing features from 3D part designs using a Graph Neural Network (GNN), (ii) identifies the manufacturing processes necessary to produce all features in the part using a Convolutional Neural Network (CNN) that considers shape, material properties, and quality information, and (iii) outputs an ordered manufacturing sequence that can produce the designed part with the help of sequence mining. Using these methods, the knowledge required to enable automated manufacturing process selection is easily scalable and updatable without requiring manual population of ad-hoc or rule-based descriptions. We present exemplar implementations of the proposed framework by suggesting manufacturing sequences for discrete parts with multiple features. The suggested manufacturing sequences demonstrate the potential of the proposed framework for use in future on-demand cyber manufacturing applications.

Suggested Citation

  • Changxuan Zhao & Mahmoud Dinar & Shreyes N. Melkote, 2024. "Deep learning and sequence mining for manufacturing process and sequence selection," International Journal of Production Research, Taylor & Francis Journals, vol. 62(14), pages 5293-5314, July.
    • Handle: RePEc:taf:tprsxx:v:62:y:2024:i:14:p:5293-5314
    DOI: 10.1080/00207543.2023.2290700
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