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Enhancing fatigue life of SS316L via shot peening and machine learning

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This study presents a method to improve the fatigue life of SS316L samples fabricated by Laser Powder Bed Fusion (LPBF). Shot peening applied after heat treatment at 900°C for 1 h enhanced fatigue life to 3,829,354 cycles. A key innovation of this work is the investigation of shot peening coverage (C), which reached up to 68%—a critical factor in improving fatigue performance. To understand the role of partial residual stress relief, the same optimized shot peening parameters were applied to as-built samples and those heat-treated at a lower temperature of 600°C. This enabled the evaluation of how residual stress states affect fatigue behavior. Using material condition, peening depth, C, and peening duration as input features, machine learning models (Random Forest and XGBoost) achieved over 90% accuracy, offering a predictive framework for optimizing fatigue life in additively manufactured metals.
Title: Enhancing fatigue life of SS316L via shot peening and machine learning
Description:
This study presents a method to improve the fatigue life of SS316L samples fabricated by Laser Powder Bed Fusion (LPBF).
Shot peening applied after heat treatment at 900°C for 1 h enhanced fatigue life to 3,829,354 cycles.
A key innovation of this work is the investigation of shot peening coverage (C), which reached up to 68%—a critical factor in improving fatigue performance.
To understand the role of partial residual stress relief, the same optimized shot peening parameters were applied to as-built samples and those heat-treated at a lower temperature of 600°C.
This enabled the evaluation of how residual stress states affect fatigue behavior.
Using material condition, peening depth, C, and peening duration as input features, machine learning models (Random Forest and XGBoost) achieved over 90% accuracy, offering a predictive framework for optimizing fatigue life in additively manufactured metals.

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