查看更多>>摘要: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.