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Prediction of residual stresses in welded structures based on neural network: a review

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Abstract

The wide application of welding manufacturing across key significant industries has aroused an increasing concern on weldments’ reliability. Weld residual stress, as a critical factor, is closely related to structural and material-level failures of weldments. Hence, the prediction of residual stress is necessary for ensuring weldments’ reliability. Neural network models have emerged as promising methods to achieve this prediction. In this review, the prediction of residual stress based on neural network models is reviewed to summarize current state and uncover their limitations. In terms of these limitations, this review gives substantial suggestions for future research on neural network modeling.

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Acknowledgements

All authors are grateful to Class III Peak Discipline of Shanghai—Materials Science and Engineering (High-Energy Beam Intelligent Processing and Green Manufacturing) for its support in conducting experimental measurements.

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Authors and Affiliations

  1. School of Materials Science and Engineering, Shanghai University of Engineering Science, Shanghai, 201620, China

    Yuli Qin & Chunwei Ma

  2. Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, China

    Lin Mei

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  1. Yuli Qin
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Contributions

Yuli Qin was involved in methodology, software, project administration, data curation, writing—original draft, validation, investigation, writing—original draft and software. Chunwei Ma contributed to material preparation, resources, supervision and project administration. Lin Mei took part in conceptualization, resources, supervision and validation.

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Correspondence to Chunwei Ma.

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I, along with my coauthors, have no relevant financial or non-financial interests to disclose.

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Qin, Y., Ma, C. & Mei, L. Prediction of residual stresses in welded structures based on neural network: a review. J Mater Sci (2024). https://doi.org/10.1007/s10853-024-10178-6

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