Machine learning based prediction of carbon concentration in carburized steel

The carbon concentration and hardness of a part’s surface can be enhanced through the carburizing-quenching process. The mechanical properties are affected by the carbon concentration distribution in the surface layer, which directly determines wear resistance and fatigue life. Carbon concentration...

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Published in:	Scientific reports Vol. 15; no. 1; pp. 33678 - 19
Main Authors:	Zhang, Yingtao, Tang, Zirong, Yin, Qing, Wang, Yanjie, Nie, Zhenguo
Format:	Journal Article
Language:	English
Published:	London Nature Publishing Group UK 29.09.2025 Nature Publishing Group Nature Portfolio
Subjects:	20Cr2Ni4A 639/166 639/301 Accuracy Algorithms Carbon Carburizing Datasets Finite element method Hardness Heat treating Heat treatment Heat treatments Heuristic Humanities and Social Sciences Learning algorithms Machine learning Mathematical models Mean shift clustering Mechanical properties multidisciplinary Neural networks Numerical calculation Optimization Predictions Science Science (multidisciplinary) Simulation Slime molds Slime mould algorithm Titanium alloys 20Cr2Ni4A Mean shift clustering Carburizing Numerical calculation Slime mould algorithm
ISSN:	2045-2322, 2045-2322
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Abstract	The carbon concentration and hardness of a part’s surface can be enhanced through the carburizing-quenching process. The mechanical properties are affected by the carbon concentration distribution in the surface layer, which directly determines wear resistance and fatigue life. Carbon concentration distribution can be calculated using the FEA (Finite Element Analysis) method with an error no greater than 10% for the heat treatment engineer. However, predicting the results using machine learning method is more efficient and easier. In this paper, the carburizing numerical model is established for a 20Cr2Ni4A cylindrical part. The microstructures and hardness gradient were tested to verify the accuracy of the simulation model for the furnace samples after the carburizing process. By establishing an accurate numerical model of the carburizing process, multiple sets of process parameter combinations were designed for three different shapes: square, circle, and trapezoid. Large-scale calculations were performed, resulting in a total of 530,226 sets of samples obtained. Finally, a parameter adaptive BPNN (Backpropagation Neural Network) method based on mean shift clustering and slime mould algorithm is proposed for rapid prediction of carbon concentration distribution on the surface of parts after carburizing. By comparing the prediction accuracy and training time with other methods, the feasibility and effectiveness of the method are verified.
AbstractList	The carbon concentration and hardness of a part’s surface can be enhanced through the carburizing-quenching process. The mechanical properties are affected by the carbon concentration distribution in the surface layer, which directly determines wear resistance and fatigue life. Carbon concentration distribution can be calculated using the FEA (Finite Element Analysis) method with an error no greater than 10% for the heat treatment engineer. However, predicting the results using machine learning method is more efficient and easier. In this paper, the carburizing numerical model is established for a 20Cr2Ni4A cylindrical part. The microstructures and hardness gradient were tested to verify the accuracy of the simulation model for the furnace samples after the carburizing process. By establishing an accurate numerical model of the carburizing process, multiple sets of process parameter combinations were designed for three different shapes: square, circle, and trapezoid. Large-scale calculations were performed, resulting in a total of 530,226 sets of samples obtained. Finally, a parameter adaptive BPNN (Backpropagation Neural Network) method based on mean shift clustering and slime mould algorithm is proposed for rapid prediction of carbon concentration distribution on the surface of parts after carburizing. By comparing the prediction accuracy and training time with other methods, the feasibility and effectiveness of the method are verified. Abstract The carbon concentration and hardness of a part’s surface can be enhanced through the carburizing-quenching process. The mechanical properties are affected by the carbon concentration distribution in the surface layer, which directly determines wear resistance and fatigue life. Carbon concentration distribution can be calculated using the FEA (Finite Element Analysis) method with an error no greater than 10% for the heat treatment engineer. However, predicting the results using machine learning method is more efficient and easier. In this paper, the carburizing numerical model is established for a 20Cr2Ni4A cylindrical part. The microstructures and hardness gradient were tested to verify the accuracy of the simulation model for the furnace samples after the carburizing process. By establishing an accurate numerical model of the carburizing process, multiple sets of process parameter combinations were designed for three different shapes: square, circle, and trapezoid. Large-scale calculations were performed, resulting in a total of 530,226 sets of samples obtained. Finally, a parameter adaptive BPNN (Backpropagation Neural Network) method based on mean shift clustering and slime mould algorithm is proposed for rapid prediction of carbon concentration distribution on the surface of parts after carburizing. By comparing the prediction accuracy and training time with other methods, the feasibility and effectiveness of the method are verified. The carbon concentration and hardness of a part's surface can be enhanced through the carburizing-quenching process. The mechanical properties are affected by the carbon concentration distribution in the surface layer, which directly determines wear resistance and fatigue life. Carbon concentration distribution can be calculated using the FEA (Finite Element Analysis) method with an error no greater than 10% for the heat treatment engineer. However, predicting the results using machine learning method is more efficient and easier. In this paper, the carburizing numerical model is established for a 20Cr2Ni4A cylindrical part. The microstructures and hardness gradient were tested to verify the accuracy of the simulation model for the furnace samples after the carburizing process. By establishing an accurate numerical model of the carburizing process, multiple sets of process parameter combinations were designed for three different shapes: square, circle, and trapezoid. Large-scale calculations were performed, resulting in a total of 530,226 sets of samples obtained. Finally, a parameter adaptive BPNN (Backpropagation Neural Network) method based on mean shift clustering and slime mould algorithm is proposed for rapid prediction of carbon concentration distribution on the surface of parts after carburizing. By comparing the prediction accuracy and training time with other methods, the feasibility and effectiveness of the method are verified.The carbon concentration and hardness of a part's surface can be enhanced through the carburizing-quenching process. The mechanical properties are affected by the carbon concentration distribution in the surface layer, which directly determines wear resistance and fatigue life. Carbon concentration distribution can be calculated using the FEA (Finite Element Analysis) method with an error no greater than 10% for the heat treatment engineer. However, predicting the results using machine learning method is more efficient and easier. In this paper, the carburizing numerical model is established for a 20Cr2Ni4A cylindrical part. The microstructures and hardness gradient were tested to verify the accuracy of the simulation model for the furnace samples after the carburizing process. By establishing an accurate numerical model of the carburizing process, multiple sets of process parameter combinations were designed for three different shapes: square, circle, and trapezoid. Large-scale calculations were performed, resulting in a total of 530,226 sets of samples obtained. Finally, a parameter adaptive BPNN (Backpropagation Neural Network) method based on mean shift clustering and slime mould algorithm is proposed for rapid prediction of carbon concentration distribution on the surface of parts after carburizing. By comparing the prediction accuracy and training time with other methods, the feasibility and effectiveness of the method are verified.
ArticleNumber	33678
Author	Yin, Qing Zhang, Yingtao Tang, Zirong Wang, Yanjie Nie, Zhenguo
Author_xml	– sequence: 1 givenname: Yingtao surname: Zhang fullname: Zhang, Yingtao organization: College of Mechanical & Electrical Engineering, Hohai University – sequence: 2 givenname: Zirong surname: Tang fullname: Tang, Zirong email: tangzirong@hhu.edu.cn organization: College of Mechanical & Electrical Engineering, Hohai University – sequence: 3 givenname: Qing surname: Yin fullname: Yin, Qing organization: College of Mechanical & Electrical Engineering, Hohai University – sequence: 4 givenname: Yanjie surname: Wang fullname: Wang, Yanjie organization: College of Mechanical & Electrical Engineering, Hohai University – sequence: 5 givenname: Zhenguo surname: Nie fullname: Nie, Zhenguo email: zhenguonie@tsinghua.edu.cn organization: Department of Mechanical Engineering, Tsinghua University
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Cites_doi	10.1016/j.jnca.2020.102854 10.1016/j.future.2020.03.055 10.1007/s11665-023-08510-3 10.1016/j.petrol.2019.06.014 10.1109/ULTSYM.2019.8926078 10.1007/s11665-021-06035-1 10.2320/matertrans.M2016361 10.1007/s11665-017-2729-0 10.1115/PVP2018-84226 10.1007/s11665-009-9476-9 10.1108/IJSI-06-2023-0048 10.1115/1.4050560 10.1016/j.triboint.2022.107576 10.1007/s00521-015-1920-1 10.3390/f14010051 10.1016/j.cma.2022.115499 10.1109/TNNLS.2016.2634548 10.1111/ffe.13541 10.3390/coatings11081003 10.1016/j.cma.2021.114218 10.1098/rsta.2022.0406 10.1142/S0217984917400802 10.1016/j.ijfatigue.2022.106938 10.1109/ACCESS.2019.2948475 10.1098/rsta.2022.0386 10.1108/ILT-10-2023-0328 10.3390/ijerph20021043 10.1016/j.ijfatigue.2019.105432 10.1007/s11665-021-06236-8
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Snippet	The carbon concentration and hardness of a part’s surface can be enhanced through the carburizing-quenching process. The mechanical properties are affected by... The carbon concentration and hardness of a part's surface can be enhanced through the carburizing-quenching process. The mechanical properties are affected by... Abstract The carbon concentration and hardness of a part’s surface can be enhanced through the carburizing-quenching process. The mechanical properties are...
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SubjectTerms	20Cr2Ni4A 639/166 639/301 Accuracy Algorithms Carbon Carburizing Datasets Finite element method Hardness Heat treating Heat treatment Heat treatments Heuristic Humanities and Social Sciences Learning algorithms Machine learning Mathematical models Mean shift clustering Mechanical properties multidisciplinary Neural networks Numerical calculation Optimization Predictions Science Science (multidisciplinary) Simulation Slime molds Slime mould algorithm Titanium alloys
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Title	Machine learning based prediction of carbon concentration in carburized steel
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