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April 29, 2026Fatigue & Fracture of Engineering Materials & Structures

Predicting Fatigue Life in Additively Manufactured 316L Stainless Steel: An Integration of Machine Learning

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Authors

ITItthidet ThawonDVDuy VoRWRamnarong Wanison

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Overview

A machine learning framework predicts fatigue life in additively manufactured stainless steel, indicating key factors like elongation and applied stress.

Key Points

  • The aim is to develop a machine learning framework for predicting the fatigue life of additively manufactured 316L stainless steel based on mechanical properties and loading conditions.
  • Proposed a machine learning framework for fatigue life prediction without requiring detailed AM process data.
  • Utilized a literature-derived dataset covering multiple AM techniques for model training and comparison.
  • Employed multilayer perceptron for predictive performance and Explainable AI for variable interpretation.
  • Identified applied stress, elongation, and strength-related properties as dominant factors in fatigue life.
  • Demonstrated feasibility of property-based fatigue life prediction across various additive manufacturing processes.

Cite This Study

Thawon et al. (2026) studied this question.

synapsesocial.com/papers/69f154e0879cb923c49452e9https://doi.org/10.1111/ffe.70290
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Prediction of Mechanical Properties of Additively Manufactured 316L Steel Based on Microstructural Features Using Data-Driven Approaches2025
  2. 2Predicting Fatigue Life of Additively Manufactured Metals Using Data‐Driven and Defect‐Based Models: a Comprehensive Review2026
  3. 3Machine Learning-Enabled Prognostication of Tensile Strength in 316L Stainless Steel Through Additive Manufacturing Processes2026
  4. 4Fatigue performance of metal additive manufacturing: a comprehensive overview2024 · 94 citations
  5. 5A unified creep and fatigue life prediction approach for 316 austenitic stainless steel using machine and deep learning2024 · 3 citations