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The stress–strain curve (SSC) prediction for additively manufactured as-build metal materials with laser powder bed fusion (LPBF) is a lengthy and tedious process. It involves the sophisticated representative volume element (RVE) reconstruction of complex experimental microstructures for subsequent state-of-the-art crystal plasticity simulations with hyperparameter tunings in the appropriate physical model. However, even with a well-fitted model, simulations with different RVEs or temperatures, for example, are too time-consuming and computationally intensive. In recent years, several attempts were directed towards the SSC predictions with machine learning (ML) tools to speed up this process. Mainly, the artificial neural networks (ANN) were reported so far for this purpose. Here, we present our version to predict the temperature dependence of SSCs for LPBF fabricated industrially important Hastelloy X with various ML methods. Compared to previously reported studies on this matter with direct link between the microstructures and SSCs, we directly link only experimental conditions and predicted SSCs, which could be more preferable for some application scenarios discussed below. It was found that due to the structure and “small” size of our training dataset, the decision tree-based ML regressors worked better than other popular ML methods.

Rights:

• Creative Commons BY-NC-ND Attribution-NonCommercial-NoDerivs 4.0 International
  

  Licensee: Springer Nature Limited Creative Commons licence CC BY-NC-ND: This licence permits any noncommercial use, sharing, distribution and reproduction in any medium or format, as long as appropriate credit is given to the original author(s) and the source, a link is provided to the Creative Commons licence, and any modification of the licensed material is indicated. Under this licence sharing of adapted material derived from this Article or parts of it is not allowed. Please read the full licence for further details at - http://creativecommons.org/licenses/by-nc-nd/4.0/

Keyword: additive manufacturing, crystal plasticity simulations, representative volume element reconstruction, machine learning regression, stress–strain curve simulation/prediction

Date published: 2024-11-27

Publisher: Springer Science and Business Media LLC

Journal:

• Scientific Reports (ISSN: 20452322) vol. 14 29492

Funding:

• 3D積層造形プロセスのマルチフィジック - EAD040
• 積層材料 Structural Materials for Extreme Environments toward a Carbon Neutral Society AD2070

Manuscript type: Publisher's version (Version of record)

MDR DOI:

First published URL: https://doi.org/10.1038/s41598-024-80098-7

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Updated at: 2024-12-18 08:30:38 +0900

Published on MDR: 2024-12-18 08:30:39 +0900