A generic and extensible model for the martensite start temperature incorporating thermodynamic data mining and deep learning framework  

作　　者：Chenchong Wang Kaiyu Zhu Peter Hedström Yong Li Wei Xu 

机构地区：[1]State Key Laboratory of Rolling and Automation,Northeastern University,Shenyang 110819,China [2]Department of Materials Science and Engineering,KTH Royal Institute of Technology,Stockholm 10044,Sweden

出　　处：《Journal of Materials Science & Technology》2022年第33期31-43,共13页材料科学技术（英文版）

基　　金：financially supported by the National Natural Science Foundation of China(Nos.51801019 and U1808208)。

摘　　要：The martensite start temperature is a critical parameter for steels with metastable austenite.Although numerous models have been developed to predict the martensite start(Ms)temperature,the complexity of the martensitic transformation greatly limits their performance and extensibility.In this work,we apply deep data mining of thermodynamic calculations and deep learning to develop a generic model for Msprediction.Deep data mining was used to establish a hierarchical database with three levels of information.Then,a convolutional neural network model,which can accurately treat the hierarchical data structure,was used to obtain the final model.By integrating thermodynamic calculations,traditional machine learning and deep learning modeling,the final predictor model shows excellent generalizability and extensibility,i.e.model performance both within and beyond the composition range of the original database.The effects of 15 alloying elements were considered successfully using the proposed methodology.The work suggests that,with the help of deep data mining considering the physical mechanisms,deep learning methods can partially mitigate the challenge with limited data in materials science and provide a means for solving complex problems with small databases.

关 键 词：Martensite transformation Data mining Deep learning EXTENSIBILITY Small-sample problem 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论] TP18[自动化与计算机技术—计算机科学与技术] TG142.2[一般工业技术—材料科学与工程]