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

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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.

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(Adapted from Ding et al., 2022)

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Data availability

The datasets about the melt pool width in DED-LB/p and the relative densities of parts fabricated by different machines are open-accessed. The dataset about the melt pool width in DED-LB/w is available upon reasonable request from the authors.

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Acknowledgements

The authors gratefully acknowledge funding from the Natural Sciences and Engineering Research Council (NSERC) of Canada [Grant numbers: RGPIN-2019-06601] and the in-kind support of University West (Dr. Morgan Nilsen and Dr. Fredrik Sikström) under the Eureka! SMART project (S0410) titled “TANDEM: Tools for Adaptive and Intelligent Control of Discrete Manufacturing Processes.” Meanwhile, the authors acknowledge the Ph.D. candidate Javid Akhavan at Stevens Institute of Technology, United States, for his help in processing their image datasets.

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  1. Product Design and Optimization Laboratory, School of Mechatronic Systems Engineering, Simon Fraser University, Surrey, BC, Canada

    Yifan Tang, Mostafa Rahmani Dehaghani, Pouyan Sajadi & G. Gary Wang

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Yifan Tang: writing—original draft preparation, validation, methodology, investigation, formal analysis, conceptualization. Mostafa Rahmani Dehaghani: resources, methodology, formal analysis, conceptualization. Pouyan Sajadi: resources, methodology, formal analysis, conceptualization. G. Gary Wang: writing—review & editing, supervision, resources, project administration, methodology, funding acquisition, conceptualization.

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Tang, Y., Rahmani Dehaghani, M., Sajadi, P. et al. Selecting subsets of source data for transfer learning with applications in metal additive manufacturing. J Intell Manuf (2024). https://doi.org/10.1007/s10845-024-02402-6

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