Towards accurate prediction for ultra-low carbon tempered martensite property through the cross-correlated substructures

Materials & Design

Wei Li¹  Hao Du¹  Xuejun Jin¹  Kuan Zhang²  Qi Lu³  Yuantao Xu⁴  Xingqi Jia⁴ 

[1] School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, PR China;Shanghai Key Laboratory of Materials Laser Processing and Modification, Shanghai Jiao Tong University, Shanghai 200240, PR China;Corresponding authors at: Institute of Advanced Steels and Materials, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, PR China.;Shanghai Key Laboratory of Materials Laser Processing and Modification, Shanghai Jiao Tong University, Shanghai 200240, PR China;

关键词: Laser deposition;    Martensitic steels;    Electron backscattering diffraction (EBSD);    Hardness;    Machine learning;   

DOI  :  

来源: DOAJ

【 摘 要 】

Accurately predicting properties of steels containing martensite by using models based on traditional strengthening mechanisms remains a challenge. In this study, a smart machine learning model possessing two-dimensional microstructure input terminals was developed using high-throughput experiments and machine learning on steels for low-temperature service. An algorithm based on a convolutional neural network enriched with the two-dimensional input terminals increased the prediction accuracy, achieving an average microhardness error of as low as 14.37 HV for the validation set. The improved prediction accuracy is ascribed to the comprehensive strengthening mechanism and coupling of strengthening effects contained in the multifarious input terminals. The information acquisition and cross-correlation of substructures related to strengthening mechanism played an important role. The reported strategy can deepen the cognition of the strengthening mechanism of tempered martensite. It is promising for application to different steels containing tempered martensite.

【 授权许可】

Unknown