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February 8, 2026MicromachinesOpen Access

Machine Learning-Enabled Prognostication of Tensile Strength in 316L Stainless Steel Through Additive Manufacturing Processes

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Authors

QGQing GaoCWCongyu WangJHJiayan Hu

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Overview

Demonstrates a predictive model for tensile strength in 316L stainless steel, suggesting improved accuracy in additive manufacturing applications.

Key Points

  • The aim is to develop a reliable model for predicting the tensile strength of 316L stainless steel components produced through additive manufacturing.
  • Developed a predictive model utilizing deep learning (CNN) and random forests (RF).
  • Trained on a dataset comprising 42 sets of experimental data.
  • Validated the model against an additional 12 experimental datasets.
  • Quantified predictive performance using mean squared error (MSE) and mean absolute error (MAE).
  • Achieved MSE of 0.00295 and MAE of 0.0344, implying improved predictive accuracy.
  • Showed a 3.28% reduction in MSE and a 31.88% reduction in MAE compared to using CNN alone.
  • Attained a correlation coefficient of 0.9576, indicating strong predictive capability.

Cite This Study

Gao et al. (2026) studied this question.

synapsesocial.com/papers/6988291e0fc35cd7a88492bbhttps://doi.org/10.3390/mi17020212
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