基于PSO-BP的2205双相不锈钢热变形行为预测

doi:10.13228/j.boyuan.issn0449-749x.20220509

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Abstract Aiming at the problem that the constitutive equation established by traditional mathematical and statistical methods cannot accurately reflect the hot processing properties of materials, a hot deformation prediction model with machine learning combined with optimization algorithm was proposed. A single-pass hot compression test was conducted using a Gleeble-3800 thermal simulation test machine to study the rheological behavior of 2205 duplex stainless steel under the heating temperature of 900-1 100 ℃ and the strain rate of 0.01-10 s^-1. The Arrhenius model considering strain correction and the improved BP neural network model based on particle swarm algorithm(PSO-BP) were constructed,and the predicted values of the PSO-BP model were used to draw the hot processing diagram based on dynamic material model (DMM). The prediction ability and stability of the two models were evaluated by calculating the mean square correlation coefficient(R²), root mean square error(RMSE) and average relative error(AARE). The results show that the PSO-BP model has better performance in predicting the flow properties of 2205 duplex stainless steel with R², RMSE and AARE of 0.999 79, 1.138 7 and 1.43%, respectively, which is a 2% improvement in R² and a 10.147 6 MPa and 4.979% reduction in RMSE and AARE, respectively, relative to the strain-compensated Arrhenius constitutive model. Moreover, the drawn hot processing map is in good agreement with the experimental hot processing map, and the optimal processing intervals are from 1 000 to 1 100 ℃ for processing temperature and 3.5 to 10 s^-1 for strain rate. Therefore, the proposed PSO-BP model has strong reliability and applicability, can accurately describe the hot deformation characteristics of 2205 duplex stainless steel, and provide theoretical guidance and technical support for the forging, rolling and other processes of 2205 duplex stainless steel.

Key words： 2205 duplex stainless steel hot deformation flow stress constitutive model neural network

Received: 01 August 2022

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	Articles by authors
	JI Ya-feng
	WANG Xiao-jun
	MENG Yuan
	WANG Hai-shen
	LIU Yu
	LI Xu

Cite this article:

JI Ya-feng,WANG Xiao-jun,MENG Yuan等. Prediction of hot deformation behavior of 2205 duplex stainless steel based on PSO-BP[J]. Iron and Steel, 2023, 58(2): 96-103.

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http://www.chinamet.cn/Jweb_gt/EN/10.13228/j.boyuan.issn0449-749x.20220509 OR http://www.chinamet.cn/Jweb_gt/EN/Y2023/V58/I2/96

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