Process optimization of quenching and partitioning by machine learning aided with orthogonal experimental design
In: Materials Research Express, Jg. 11 (2024), Heft 1, S. 016519
academicJournal
Zugriff:
Owing to a balance between toughness and strength, quenching and partitioning (Q&P) is promising in steel industry. However, for a new material or a new process, it remains challenging how to get the best parameters in low cost way. Here, a novel workflow combining orthogonal experimental design with artificial neural network and particle swarm optimization, was adopted to explore the relationship between quenching and partitioning process parameters and properties in Fe-0.65 wt%C-1.50 wt%Si-0.91 wt%Mn-1.08 wt%W steel. By using this method, the workload is reduced significantly. Compared with traditional process, the elongation of the steel increases by 146% times without loss in yield strength and a little improvement in ultimate tensile strength by quenching at 167 °C followed by partitioning at 367 °C for 5.0 min.
Titel: |
Process optimization of quenching and partitioning by machine learning aided with orthogonal experimental design
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Autor/in / Beteiligte Person: | Dai, Na ; Li, Jian ; Qin, Hai ; He, Guolin ; Li, Pengfei ; Wu, Zhenghua ; Wang, Shanlin |
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Zeitschrift: | Materials Research Express, Jg. 11 (2024), Heft 1, S. 016519 |
Veröffentlichung: | IOP Publishing, 2024 |
Medientyp: | academicJournal |
ISSN: | 2053-1591 |
DOI: | 10.1088/2053-1591/ad201e |
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