增材制造金属疲劳寿命预测的小样本机器学习方法研究进展

doi:10.7527/S1000-6893.2026.33842

Abstract

Abstract:

Additive Manufacturing （AM） has considerable potential for lightweight aerospace structures and the integrated fabrication of complex components. However， the fatigue life of AM metals exhibits pronounced scatter and uncertainty because of the coupled effects of processing parameters， microstructure， internal defects， and residual stresses， making reliable life predictions challenging. Machine Learning （ML） provides a promising approach for capturing the nonlinear relationships among processing， microstructure， defects， and fatigue life. Nevertheless， high testing costs， long data-acquisition cycles and cross-platform data heterogeneity have resulted in persistent challenges associated with small samples， high noise， and limited model generalizability. This review summarizes recent advances in small-sample ML methods for fatigue life prediction of AM metals， including physics-guided ML， mechanism-based data augmentation， multi-fidelity modeling， hybrid physics-data frameworks， transfer learning and meta-learning， uncertainty quantification， and probabilistic life prediction. Current issues related to incomplete database metadata， inconsistent evaluation benchmarks， and the disconnect between experimental design and algorithm assessment are also discussed. Finally， future directions are outlined， including standardized database development， physically consistent data fusion， and unified evaluation frameworks for engineering qualifications.

Key words: additive manufacturing, fatigue life prediction, small sample, physics-guided, multi-fidelity modeling, transfer learning, uncertainty quantification

CLC Number:

V231.95

Wei HE, Quan ZHANG, Shuzhao LAN, Zechang LI, Jinhong LI, Jiaqiang LI, Xin LI. Recent advances in small-sample machine learning methods for fatigue life prediction of additively manufactured metals[J]. Acta Aeronautica et Astronautica Sinica, 2026, 47(19): 633842.

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核心方法	解决的关键痛点	物理内涵	文献
概率化物理引导框架	小样本过拟合与数据不确定性	损失函数集成宏观疲劳公式	［94］
关键缺陷驱动的物理信息预测模型	内部缺陷主导的失效难刻画	内部缺陷特征作为物理先验	［79］
物理信息神经网络	缺乏跨材料的通用评估范式	物理先验作为底层归纳偏置	［104］
微观力学分析与机器学习结合	裂纹萌生阶段评估困难	动态追踪微观力学机制	［80］

物理内涵	核心方法	解决的关键痛点	文献
统计极值理论与体积效应	贝叶斯物理信息神经网络	跨尺寸寿命预测的分散性	［94］
物理一致性的生成式对抗网络	多保真建模	极小样本下的数据扩增	［111］
加权等效指标与后验分布比对	贝叶斯模型决策	工艺敏感性（方向/后处理）	［90］
似然函数驱动的认知风险量化	贝叶斯物理信息神经网络	航空结构件的可靠性评估	［98］

主要算法	材料	样本量	输入特征	模型优点	文献
缺陷-断裂力学约束物理信息神经网络	选择性激光熔化AlSi10Mg	12	载荷参数、关键缺陷尺寸、缺陷位置和缺陷形貌相关特征	引入线弹性断裂力学约束，提高预测结果的物理一致性	［124］
秩相关筛选	选择性激光熔化Ti-6Al-4V	29	应力幅、XCT缺陷尺寸、缺陷位置、缺陷数量、缺陷形貌相关特征	提高单个疲劳样本的信息密度，并解释缺陷对寿命分散性的影响	［125］
支持向量回归	电子束粉末床熔融Ti2448	34	密度、孔隙率、屈服应力和疲劳应力	小样本条件下的快速非线性回归和工程参数敏感性分析	［126］
堆叠自编码器深度神经网络	激光粉末床熔融AlSi10Mg	89	应力幅、屈服强度、抗拉强度、延伸率、残余应力等特征	融合力学性能、残余应力、表面粗糙度和后处理状态等多源特征	［77］
Dropout神经网络	激光粉末床熔融Ti-6Al-4V	197	表面粗糙度、孔隙尺寸、孔隙投影面积、孔隙统计特征	可同时给出寿命估计和预测不确定性	［127］

Recent advances in small-sample machine learning methods for fatigue life prediction of additively manufactured metals

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