首页|Machine learning for predicting fatigue properties of additively manufactured materials

Machine learning for predicting fatigue properties of additively manufactured materials

扫码查看
原文链接
  • NETL
  • NSTL
  • 万方数据
Fatigue properties of materials by Additive Manufacturing(AM)depend on many fac-tors such as AM processing parameter,microstructure,residual stress,surface roughness,porosi-ties,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 leverag-ing the power of Machine Learning(ML)has gained increasing attentions.A comprehensive over-view 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),Convolu-tional Neural Network(CNN),Adaptive Network-Based Fuzzy Inference System(ANFIS),Sup-port 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.

Additive manufacturingMachine learningFatigue lifeFatigue crack growth ratePrediction

Min YI、Ming XUE、Peihong CONG、Yang SONG、Haiyang ZHANG、Lingfeng WANG、Liucheng ZHOU、Yinghong LI、Wanlin GUO

展开 >

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

展开 >

国家科技重大专项国家重点研发计划National Overseas Youth Talents Program,ChinaResearch Fund of State Key Laboratory of Mechanics and Control for Aerospace Structures,China江苏高校优势学科建设工程项目High Performance Computing Platform of Nanjing University of Aeronautics and Astronautics,China

J2019-IV-0014-00822022YFB4600700MCMS-I-0422K01

2024

10.1016/j.cja.2023.11.001

中国航空学报(英文版)
中国航空学会

中国航空学报(英文版)

CSTPCDEI
影响因子：0.847
ISSN：1000-9361
年,卷(期)：2024.37(4)
  • 119