A physically based constitutive model for 41CrS4 steel based on particle swarm optimization algorithm

Abstract Utilizing the Gleeble-3500 thermal simulation apparatus, a thermal compression assay was performed on 41CrS4 steel within the temperature range of 900 °C to 1200 °C, featuring a strain rate of 0.01 to 5 s −1 , to derive its flow stress curve. The evaluation of the Arrhenius equation parameters was adeptly carried out by deploying a sophisticated particle swarm optimization algorithm. Through rigorous analysis, the correlation coefficient and the mean absolute deviation were calculated to quantify the alignment between the predictive accuracy of the developed model and the empirical data. The findings demonstrate the ability of the particle swarm optimization algorithm to significantly enhance the precision of the constitutive model. This augmented level of accuracy substantively increases the model’s utility and reliability for simulations of high-temperature material forming processes.

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Publication Details

Journal
Materials Research Express
Published
2024-10-01
DOI
https://doi.org/10.1088/2053-1591/ad8396
Primary Topic
Metallurgy and Material Forming
Type
article
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article

A physically based constitutive model for 41CrS4 steel based on particle swarm optimization algorithm

Peipei Yang, Hongchen Pan, Xiaoxiao Wei, Fan Tan
Materials Research Express
Metallurgy and Material Forming
article

A physically based constitutive model for 41CrS4 steel based on particle swarm optimization algorithm

Peipei Yang, Hongchen Pan, Xiaoxiao Wei, Fan Tan
article en

Abstract

Abstract Utilizing the Gleeble-3500 thermal simulation apparatus, a thermal compression assay was performed on 41CrS4 steel within the temperature range of 900 °C to 1200 °C, featuring a strain rate of 0.01 to 5 s −1 , to derive its flow stress curve. The evaluation of the Arrhenius equation parameters was adeptly carried out by deploying a sophisticated particle swarm optimization algorithm. Through rigorous analysis, the correlation coefficient and the mean absolute deviation were calculated to quantify the alignment between the predictive accuracy of the developed model and the empirical data. The findings demonstrate the ability of the particle swarm optimization algorithm to significantly enhance the precision of the constitutive model. This augmented level of accuracy substantively increases the model’s utility and reliability for simulations of high-temperature material forming processes.

Materials Research ExpressVol. 11(10)
Electric Power Research Institute (US), State Grid Corporation of China (China) (CN), Shanghai Jiao Tong University (CN), Lanzhou University of Technology (CN), Shanghai Electric (China) (CN), SAIC Motor (China) (CN)
Openalex Percentile: Top 81%
Metallurgy and Material Forming
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A physically based constitutive model for 41CrS4 steel based on particle swarm optimization algorithm — Peipei Yang, Hongchen Pan, et al. · Materials Research Express (2024) | TGRS Research Map | TGRS