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<p>The lack of consistency in alloy’s microstructure and properties has led to a degree of conservatism in adopting additive manufacturing (AM) in critical sectors including marine, aerospace, and nuclear industries. This highlights the significance of modeling approaches in predicting the behavior of different alloys produced using AM. In this regard, recent promising accomplishments of machine learning (ML) methods in a wide variety of tasks exemplified by computer vision and natural language processing (NLP) have rendered these tools as an alternative for conventional modeling frameworks already used in AM. This work proposes an intelligent algorithm for predicting the melt pool geometry of 316L stainless steels fabricated by laser powder bed fusion (LPBF). To fulfill this goal, a benchmark dataset is introduced, which is experiment-oriented and alloy-specific. This dataset contains the state-of-the-art melt pool geometry reported in the literature as well as some experimental results conducted for this study. The proposed algorithm was validated on this dataset and its performance was compared with the ML frameworks deployed recently in AM. Results demonstrated the superiority of the proposed methodology. This approach enhances the repeatability and consistency of LPBF and facilitates process parameter optimization to develop desired properties.</p>

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Melt pool intelligent design of additively manufactured 316L stainless steels

  • 期刊论文

  • 发表日期：

    2025年6月17日

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  • Amir Zarei,
  • Parisa Moazzen,
  • Peyman Setoodeh,
  • Mohsen Mohammadi

摘要

The lack of consistency in alloy’s microstructure and properties has led to a degree of conservatism in adopting additive manufacturing (AM) in critical sectors including marine, aerospace, and nuclear industries. This highlights the significance of modeling approaches in predicting the behavior of different alloys produced using AM. In this regard, recent promising accomplishments of machine learning (ML) methods in a wide variety of tasks exemplified by computer vision and natural language processing (NLP) have rendered these tools as an alternative for conventional modeling frameworks already used in AM. This work proposes an intelligent algorithm for predicting the melt pool geometry of 316L stainless steels fabricated by laser powder bed fusion (LPBF). To fulfill this goal, a benchmark dataset is introduced, which is experiment-oriented and alloy-specific. This dataset contains the state-of-the-art melt pool geometry reported in the literature as well as some experimental results conducted for this study. The proposed algorithm was validated on this dataset and its performance was compared with the ML frameworks deployed recently in AM. Results demonstrated the superiority of the proposed methodology. This approach enhances the repeatability and consistency of LPBF and facilitates process parameter optimization to develop desired properties.