Quantification of the influence of laser shock peening parameters on residual stresses in thick AA 7050 specimens: An experimental and explainable machine learning-based approach

2026/01/01 by Claudia Barile, Simone Carone, Caterina Casavola +1

paper · doi:10.1016/j.mechmat.2025.105536

crossref created 2025/10/28 · crossref deposited 2025/12/04 · crossref issued 2026/01/01 · crossref published 2026/01/01 · crossref published-print 2026/01/01 · crossref indexed 2026/08/05

Citations

• Machine learning in prediction of residual stress in laser shock peening for maximizing residual compressive stress formation
• From local explanations to global understanding with explainable AI for trees
• A Unified Approach to Interpreting Model Predictions
• Scikit-learn: Machine Learning in Python
• Laser shock processing and its effects on microstructure and properties of metal alloys: a review