| Fe-Mn-C-Al系TWIP钢热处理工艺参数优化 |
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| 引用本文： | 王凯,王荣吉,周童,彭松.Fe-Mn-C-Al系TWIP钢热处理工艺参数优化[J].金属热处理,2022,47(9):31-35. |
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| 作者姓名： | 王凯 王荣吉 周童 彭松 |
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| 作者单位： | 中南林业科技大学 机电工程学院, 湖南 长沙 410004 |
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| 基金项目： | 湖南省教育厅科学研究重点项目(14A157) |
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| 摘 要： | 为提高TWIP钢的屈服强度同时保留较好的塑性,利用BP神经网络和遗传算法对热处理工艺参数进行优化。以退火温度、保温时间和冷却方式为输入,屈服强度和伸长率的乘积为输出,建立3-4-1的BP神经网络模型,再通过遗传算法寻优,得到屈服强度和伸长率的乘积最大时TWIP钢的热处理工艺参数组合。结果表明,优化后的热处理工艺为:退火温度768 ℃、保温时间35 min、冷却方式为炉冷,并通过试验验证了预测结果的准确性。
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| 关 键 词： | TWIP钢 BP神经网络 遗传算法 热处理工艺 参数优化 |
| 收稿时间： | 2022-04-14 |
Optimization of heat treatment process parameters of Fe-Mn-C-Al series TWIP steel |
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| 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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| Authors: | Wang Kai Wang Rongji Zhou Tong Peng Song |
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| Affiliation: | School of Mechanical and Electrical Engineering, Central South University of Forestry and Technology, Changsha Hunan 410004, China |
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| 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. |
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| Keywords: | TWIP steel BP neural network genetic algorithm heat treatment process parameters optimization |
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