Next Article in Journal
Lattice Dynamics, Mechanical Properties, Electronic Structure and Magnetic Properties of Equiatomic Quaternary Heusler Alloys CrTiCoZ (Z = Al, Si) Using First Principles Calculations
Previous Article in Journal
Multifunctional Cu2SnS3 Nanoparticles with Enhanced Photocatalytic Dye Degradation and Antibacterial Activity
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Prediction of Hardenability Curves for Non-Boron Steels via a Combined Machine Learning Model

1
Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing 100083, China
2
Department of Mathematical Sciences, Karakoram International University, Gilgit 15100, Pakistan
3
Yangjiang Branch, Guangdong Laboratory for Materials Science and Technology (Yangjiang Advanced Alloys Laboratory), Yangjiang 529500, China
4
School of Materials Science and Engineering, University of Science and Technology Beijing, Beijing 100083, China
*
Authors to whom correspondence should be addressed.
Materials 2022, 15(9), 3127; https://doi.org/10.3390/ma15093127
Submission received: 9 March 2022 / Revised: 18 April 2022 / Accepted: 21 April 2022 / Published: 26 April 2022

Abstract

Hardenability is one of the most basic criteria influencing the formulation of the heat treatment process and steel selection. Therefore, it is of great engineering value to calculate the hardenability curves rapidly and accurately without resorting to any laborious and costly experiments. However, generating a high-precision computational model for steels with different hardenability remains a challenge. In this study, a combined machine learning (CML) model including k-nearest neighbor and random forest is established to predict the hardenability curves of non-boron steels solely on the basis of chemical compositions: (i) random forest is first applied to classify steel into low- and high-hardenability steel; (ii) k-nearest neighbor and random forest models are then developed to predict the hardenability of low- and high-hardenability steel. Model validation is carried out by calculating and comparing the hardenability curves of five steels using different models. The results reveal that the CML model works well for its distinguished prediction performance with precise classification accuracy (100%), high correlation coefficient (≥0.981), and low mean absolute errors (≤3.6 HRC) and root-mean-square errors (≤3.9 HRC); it performs better than JMatPro and empirical formulas including the ideal critical diameter method and modified nonlinear equation. Therefore, this study demonstrates that the CML model combining material informatics and data-driven machine learning can rapidly and efficiently predict the hardenability curves of non-boron steel, with high prediction accuracy and a wide application range. It can guide process design and machine part selection, reducing the cost of trial and error and accelerating the development of new materials.
Keywords: hardenability; machine learning; JMatPro; empirical formulas hardenability; machine learning; JMatPro; empirical formulas

Share and Cite

MDPI and ACS Style

Geng, X.; Wang, S.; Ullah, A.; Wu, G.; Wang, H. Prediction of Hardenability Curves for Non-Boron Steels via a Combined Machine Learning Model. Materials 2022, 15, 3127. https://doi.org/10.3390/ma15093127

AMA Style

Geng X, Wang S, Ullah A, Wu G, Wang H. Prediction of Hardenability Curves for Non-Boron Steels via a Combined Machine Learning Model. Materials. 2022; 15(9):3127. https://doi.org/10.3390/ma15093127

Chicago/Turabian Style

Geng, Xiaoxiao, Shuize Wang, Asad Ullah, Guilin Wu, and Hao Wang. 2022. "Prediction of Hardenability Curves for Non-Boron Steels via a Combined Machine Learning Model" Materials 15, no. 9: 3127. https://doi.org/10.3390/ma15093127

APA Style

Geng, X., Wang, S., Ullah, A., Wu, G., & Wang, H. (2022). Prediction of Hardenability Curves for Non-Boron Steels via a Combined Machine Learning Model. Materials, 15(9), 3127. https://doi.org/10.3390/ma15093127

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop