文献类型：期刊文献

英文题名：High-temperature high-cycle fatigue performance and machine learning-based fatigue life prediction of additively manufactured Hastelloy X

作者：Lei, Liming[1,3,4];Li, Bo[2,4,5];Wang, Haijie[2];Huang, Guoqing[2];Xuan, Fuzhen[2,4]

机构：[1]Taihang Lab, Chengdu 610213, Peoples R China;[2]East China Univ Sci & Technol, Sch Mech & Power Engn, Shanghai 200237, Peoples R China;[3]AECC Commercial Aircraft Engine Co Ltd, Shanghai 200241, Peoples R China;[4]Shanghai Collaborat Innovat Ctr High end Equipment, Shanghai 200237, Peoples R China;[5]East China Univ Sci & Technol, Shanghai 200237, Peoples R China

年份：2024

卷号：178

外文期刊名：INTERNATIONAL JOURNAL OF FATIGUE

收录：;EI(收录号：20234314954430);WOS:【SCI-EXPANDED(收录号:WOS:001102038000001)】；

基金：This work is sponsored by Equipment Pre -research Sharing Tech- nology Key Project (Grant No. JZX7Y20210422004601) , National Nat- ural Science Foundation of China (Grant No. 52175140) , Fundamental Research Funds for the Central Universities in China (Grant No. JKG01231610) , National Key R & D Program of China (Grant No. 2022YFB4602102) , and Pre research project of Civil Aerospace Tech- nology (Grant No. D020301) .

语种：英文

外文关键词：Fatigue life; Laser powder bed fusion; Additive manufacturing; High temperature; Machine learning

摘要：Uncertainties in fatigue life of laser powder bed fusion (L-PBF) additively manufactured parts arise from microstructural heterogeneities and randomly dispersed defects generated during the L-PBF. Stress-controlled fatigue performances of L-PBF-built Hastelloy X were examined in 400 degrees C and 600 degrees C high-temperature atmo-spheres. Given the inherent anisotropy of the L-PBF-built Hastelloy X, the fatigue tests were performed on the samples with distinct building orientations. Fatigue-induced damage evolution characteristics were meticulously analyzed after drawing the S-N curves with a 0.1 stress ratio, a 20 Hz loading frequency and varied stress am-plitudes. We established a database of the fatigue test results with material properties of the L-PBF-built Has-telloy X to build a machine learning (ML) framework for fatigue life prediction, skipping the traditional fatigue-life mathematical models that are less applicable to L-PBF-built metals. For capturing the intricate influence of diverse factors on the fatigue life accurately, deep neural network (DNN) and support vector machine (SVM) were utilized and optimized within the ML-based framework. By comparing our ML-based approach with traditional methods for life prediction, it is indicated that ML techniques can transcend the physical limitations associated with the fatigue processes while simultaneously offering a unified depiction thereof.