Optimization of heat treatment process parameters of Fe-Mn-C-Al series TWIP steel

doi:10.13251/j.issn.0254-6051.2022.09.006

Abstract

Abstract: In order to improve the yield strength and mean while retain the better plasticity of TWIP steel, BP neural network and genetic algorithm were used to optimize heat treatment process parameters. Taking annealing temperature, holding time and cooling method as input, the product of yield strength and elongation as output, a 3-4-1 BP neural network model was established. Through the optimization of genetic algorithm, the heat treatment process parameters with the maximum product of yield strength and elongation were obtained. The results show that the optimized heat treatment process parameters are annealing temperature of 768 ℃, holding time of 35 min and furnace cooling method. And the accuracy of the prediction result is verified by experiments.

Key words: TWIP steel, BP neural network, genetic algorithm, heat treatment process, parameters optimization

CLC Number:

Wang Kai, Wang Rongji, Zhou Tong, Peng Song. Optimization of heat treatment process parameters of Fe-Mn-C-Al series TWIP steel[J]. Heat Treatment of Metals, 2022, 47(9): 31-35.

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