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Selecting Subsets of Source Data for Transfer Learning with Applications in Metal Additive Manufacturing

Journal of Intelligent Manufacturing(2024)

Simon Fraser University

Cited 0|Views18
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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Metal additive manufacturing,Transfer learning,Source data selection,Pareto frontier
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要点】:该论文提出了一种基于源数据与目标数据相似性的方法,用于选择源数据子集以提高金属增材制造中迁移学习的建模性能。

方法】:研究采用了一种基于Pareto前沿的源数据选择方法,通过两个相似性度量定义的Pareto前沿上选择源数据。

实验】:该方法与基于实例的迁移学习方法(决策树回归模型)和基于模型的迁移学习方法(微调的人工神经网络)相结合,在多个金属增材制造回归任务上进行了测试,结果表明所提方法能够有效选择出有助于提升迁移学习性能的源数据子集。