<ul><li>Residual stresses within components can impact performance, and accurately determining their distributions is crucial for structural integrity.</li><li>A machine learning-based Residual Stress Generator (RSG) was developed to infer full-field stresses from limited measurements.</li><li>The RSG utilized an extensive dataset from process simulations and a ML model based on U-Net architecture for prediction.</li><li>The model showed excellent predictive accuracy on simulated stresses and effectively predicted experimentally characterized data, reducing the need for extensive experimental efforts.</li></ul>

A Machine Learning Approach to Generate Residual Stress Distributions using Sparse Characterization Data in Friction-Stir Processed Parts

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