极限抗拉强度
材料科学
残余应力
喷丸
微观结构
压痕硬度
表面粗糙度
疲劳极限
人工神经网络
弯曲
表面光洁度
多孔性
激光喷丸
复合材料
结构工程
计算机科学
机器学习
工程类
作者
Erfan Maleki,Sara Bagherifard,Seyed Mohammad Javad Razavi,Michele Bandini,Anton du Plessis,Filippo Berto,Mario Guagliano
标识
DOI:10.1016/j.ijfatigue.2022.106841
摘要
Laser powder bed fusion (LPBF) is receiving widespread attention for its capability to build components with complex geometries. Post-processing can address the adverse effects of various imperfections exhibited in LPBF parts in their as-built state, including inhomogeneous microstructure, tensile residual stresses and poor surface quality. In a recent experimental study, we investigated the influences of different post-processing techniques including heat treatment and shot peening as well as their combination on rotating bending fatigue behavior of V-notched LPBF AlSi10Mg samples. Herein, we further examined those samples regarding the specific parameters that directly influence fatigue performance with the aim to develop a deep learning based approach by means of artificial neural network. The effect of yield stress, ultimate tensile strength, elongation, porosity, microhardness, compressive residual stresses, and surface roughness and morphology were assessed and implemented in the model. Fatigue behavior of the samples was predicted and analyzed using sensitivity and parametric analyses. The obtained results reveal the high potential of deeply learned neural network for unlocking the role of post-processing on fatigue performance of LPBF AlSi10Mg samples.
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