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Modeling of Alloying Effect on Isothermal Transformation: A Case Study for Pearlitic Steel
Author(s) -
Qiao Ling,
Zhu Jingchuan,
Wang Yuan
Publication year - 2021
Publication title -
advanced engineering materials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.938
H-Index - 114
eISSN - 1527-2648
pISSN - 1438-1656
DOI - 10.1002/adem.202001299
Subject(s) - support vector machine , isothermal process , materials science , mean absolute percentage error , artificial neural network , mean squared error , isothermal transformation diagram , microstructure , transformation (genetics) , artificial intelligence , mean absolute error , machine learning , pattern recognition (psychology) , computer science , metallurgy , statistics , mathematics , thermodynamics , austenite , bainite , physics , biochemistry , chemistry , gene
Machine learning (ML) has been rapidly revolutionizing many fields and plays a vital role in materials science. To understand the microstructure of steels and even obtain optimized processing parameters, accurate predictions on the time–temperature–transformation (TTT) diagram are in urgent need. Herein, artificial neural network (ANN), support vector machine (SVM), and extreme learning machine (ELM) models are developed and compared for quantitatively predicting typical features, such as the nose tip temperature and incubation period, of TTT diagrams for pearlitic steels. As an important evaluating indicators of prediction accuracy, the correlation coefficient ( R 2 ), the root mean square error (RMSE), and the mean absolute percent error (MAPE) are used to evaluate the proposed models. The results show that the proposed SVM model exhibits higher prediction accuracy compared with the ANN and ELM and demonstrates the advantage of the machine learning approach in predictions of TTT diagrams. Moreover, a series of experiments demonstrate that the proposed SVM predictor performs favorably, with satisfactory prediction accuracy. The microstructures of the isothermal transformation products of the prepared pearlitic steel are investigated using dilatation and microscopy technique.