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Experimental-based computational prediction of the austempered steel reheating results – Laser hardening simulation.
- Source :
-
Surface & Coatings Technology . Jul2024, Vol. 487, pN.PAG-N.PAG. 1p. - Publication Year :
- 2024
-
Abstract
- Basing on the SYSWELD-driven FEM simulations and dilatometric experiments, we propose a method to predict structural changes in laser hardened austempered 30HGSNA steel containing 18.9 vol% of retained austenite. The ability to predict effects through simulation is very valuable for the design of unconventional hybrid treatments, especially when the initial microstructure is not ferritic-pearlitic. For 2 out of 3 evaluated variants, there was a satisfying prediction of a hardened – transition zone border with the prediction error in the 1.5–8.0 % range. In addition to the methodology adopted in the simulation, the impact of the increased heating rate of the austenitic transformation under continuous heating conditions was also described. The progressive shift of A c1 and A c3 temperatures towards higher values occurring at heating rates of tens of °C per second and higher was explained by the accumulation of atomic jumps. Mutual influence of the heating rate and retained austenite on A c1 temperature and hardness decrease of tempered bainite below A c1 temperature were shown. Austempered steel's hardness after rapid reheating to laser hardening temperatures and microstructural changes were presented. [Display omitted] • PN 30HGSNA steel with submicron bainite microstructure was laser hardened. • SYSWELD simulations were used to design heat treatments imitating laser hardening conditions. • SYSWELD, dilatometry and machine learning allowed prediction of hardening results. • Simulations aimed to predict depth of border between hardened and transition zones. • For 2/3 variants the error of prediction was in the range of 1.5–8.0 %. [ABSTRACT FROM AUTHOR]
- Subjects :
- *HEAT treatment
*STEEL
*LASERS
*BAINITE
*MACHINE learning
Subjects
Details
- Language :
- English
- ISSN :
- 02578972
- Volume :
- 487
- Database :
- Academic Search Index
- Journal :
- Surface & Coatings Technology
- Publication Type :
- Academic Journal
- Accession number :
- 178148634
- Full Text :
- https://doi.org/10.1016/j.surfcoat.2024.131018