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Selecting subsets of source data for transfer learning with applications in metal additive manufacturing

Author

Listed:
  • Yifan Tang

    (Simon Fraser University)

  • Mostafa Rahmani Dehaghani

    (Simon Fraser University)

  • Pouyan Sajadi

    (Simon Fraser University)

  • G. Gary Wang

    (Simon Fraser University)

Abstract

Considering data insufficiency in metal additive manufacturing (AM), transfer learning (TL) has been adopted to extract knowledge from source domains (e.g., completed printings) to improve the modeling performance in target domains (e.g., new printings). Current applications use all accessible source data directly in TL with no regard to the similarity between source and target data. This paper proposes a systematic method to find appropriate subsets of source data based on similarities between the source and limited target datasets. Such similarity is characterized by the spatial and model distance metrics. A Pareto frontier-based source data selection method is developed, where the source data located on the Pareto frontier defined by two similarity distance metrics are selected iteratively. This method is integrated into an instance-based TL method (decision tree regression model) and a model-based TL method (fine-tuned artificial neural network). Both models are then tested on several regression tasks in metal AM. Comparison results demonstrate that (1) the source data selection method is general and supports integration with various TL methods and distance metrics, (2) compared with using all source data, the proposed method can find a subset of source data from the same domain with better TL performance in metal AM regression tasks involving different processes and machines, and (3) when multiple source domains exist, the source data selection method could find the subset from one source domain to obtain comparable or better TL performance than the model constructed using data from all source domains.

Suggested Citation

  • Yifan Tang & Mostafa Rahmani Dehaghani & Pouyan Sajadi & G. Gary Wang, 2025. "Selecting subsets of source data for transfer learning with applications in metal additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 36(5), pages 3185-3206, June.
    • Handle: RePEc:spr:joinma:v:36:y:2025:i:5:d:10.1007_s10845-024-02402-6
    DOI: 10.1007/s10845-024-02402-6
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    References listed on IDEAS

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    1. Mohammad Najjartabar Bisheh & Xinya Wang & Shing I. Chang & Shuting Lei & Jianfeng Ma, 2023. "Image-based characterization of laser scribing quality using transfer learning," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2307-2319, June.
    2. Amir M. Aboutaleb & Linkan Bian & Alaa Elwany & Nima Shamsaei & Scott M. Thompson & Gustavo Tapia, 2017. "Accelerated process optimization for laser-based additive manufacturing by leveraging similar prior studies," IISE Transactions, Taylor & Francis Journals, vol. 49(1), pages 31-44, January.
    3. Yuan, Yue & Chen, Zhihua & Wang, Zhe & Sun, Yifu & Chen, Yixing, 2023. "Attention mechanism-based transfer learning model for day-ahead energy demand forecasting of shopping mall buildings," Energy, Elsevier, vol. 270(C).
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