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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Veröffentlicht in:	Scientific reports Jg. 15; H. 1; S. 33678 - 19
Hauptverfasser:	Zhang, Yingtao, Tang, Zirong, Yin, Qing, Wang, Yanjie, Nie, Zhenguo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Nature Publishing Group UK 29.09.2025 Nature Publishing Group Nature Portfolio
Schlagworte:	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
Online-Zugang:	Volltext
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