Mechanistic data-driven prediction of as-built mechanical properties in metal additive manufacturing

作     者：Xiaoyu Xie Jennifer Bennett Sourav Saha Ye Lu Jian Cao Wing Kam Liu Zhengtao Gan 

作者机构：Department of Mechanical EngineeringNorthwestern UniversityEvanstonILUSA DMG MORIHoffman EstatesILUSA Theoretical and Applied MechanicsNorthwestern UniversityEvanstonILUSA 

出 版 物：《npj Computational Materials》 (计算材料学（英文）)

年 卷 期：2021年第7卷第1期

页      面：767-778页

核心收录：

学科分类：08[工学] 080502[工学-材料学] 0805[工学-材料科学与工程（可授工学、理学学位）] 

基　　金：This study was supported by the National Science Foundation(NSF)through grants CMMI-1934367 We thank Jennifer Glerum for performing the SEM imaging and Mark Fleming for his detailed review and helpful suggestions.J.Bennett and J.Cao would like to acknowledge the support from the Army Research Laboratory(ARL W911NF-18-2-0275) J.Bennet acknowledeg the ARL Oak Ridge Associated Universities(ORAU)Journeyman Fellowship 

主　　题：additive properties prediction 

摘      要：Metal additive manufacturing provides remarkable flexibility in geometry and component design,but localized heating/cooling heterogeneity leads to spatial variations of as-built mechanical properties,significantly complicating the materials design *** this end,we develop a mechanistic data-driven framework integrating wavelet transforms and convolutional neural networks to predict location-dependent mechanical properties over fabricated parts based on process-induced temperature sequences,i.e.,thermal *** framework enables multiresolution analysis and importance analysis to reveal dominant mechanistic features underlying the additive manufacturing process,such as critical temperature ranges and fundamental thermal *** systematically compare the developed approach with other machine learning *** results demonstrate that the developed approach achieves reasonably good predictive capability using a small amount of noisy experimental *** provides a concrete foundation for a revolutionary methodology that predicts spatial and temporal evolution of mechanical properties leveraging domain-specific knowledge and cutting-edge machine and deep learning technologies.