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Review Article | Open Access

Machine learning for predicting fatigue properties of additively manufactured materials

Min YIa,b,c, Ming XUEa,b,c, Peihong CONGd,e, Yang SONGd,e, Haiyang ZHANGd,e, Lingfeng WANGf, Liucheng ZHOUf( ), Yinghong LIf, Wanlin GUOa,b,c,
State Key Laboratory of Mechanics and Control for Aerospace Structures, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
Institute for Frontier Science, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
Shenyang Engine Research Institute, Shenyang 110015, China
Liaoning Key Laboratory of Impact Dynamics on Aero Engine, Shenyang 110015, China
Science and Technology on Plasma Dynamics Laboratory, Air Force Engineering University, Xi’an 710038, China

Peer review under responsibility of Editorial Committee of CJA.

Show Author Information

Abstract

Fatigue properties of materials by Additive Manufacturing (AM) depend on many factors such as AM processing parameter, microstructure, residual stress, surface roughness, porosities, post-treatments, etc. Their evaluation inevitably requires these factors combined as many as possible, thus resulting in low efficiency and high cost. In recent years, their assessment by leveraging the power of Machine Learning (ML) has gained increasing attentions. A comprehensive overview on the state-of-the-art progress of applying ML strategies to predict fatigue properties of AM materials, as well as their dependence on AM processing and post-processing parameters such as laser power, scanning speed, layer height, hatch distance, built direction, post-heat temperature, etc., were presented. A few attempts in employing Feedforward Neural Network (FNN), Convolutional Neural Network (CNN), Adaptive Network-Based Fuzzy Inference System (ANFIS), Support Vector Machine (SVM) and Random Forest (RF) to predict fatigue life and RF to predict fatigue crack growth rate are summarized. The ML models for predicting AM materials’ fatigue properties are found intrinsically similar to the commonly used ones, but are modified to involve AM features. Finally, an outlook for challenges (i.e., small dataset, multifarious features, overfitting, low interpretability, and unable extension from AM material data to structure life) and potential solutions for the ML prediction of AM materials’ fatigue properties is provided.

Keywords

Machine learningAdditive manufacturingPredictionFatigue lifeFatigue crack growth rate

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Chinese Journal of Aeronautics
Volume 37 Issue 4,
April 2024
Pages 1-22
DOI: 10.1016/j.cja.2023.11.001
Cite this article:
YI M, XUE M, CONG P, et al. Machine learning for predicting fatigue properties of additively manufactured materials. Chinese Journal of Aeronautics, 2024, 37(4): 1-22. https://doi.org/10.1016/j.cja.2023.11.001

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Received: 25 April 2023
Revised: 14 June 2023
Accepted: 23 July 2023
Published: 07 November 2023
© 2023 Chinese Society of Aeronautics and Astronautics.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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