| 基于神经元网络和粒子群优化算法的轧制工艺—性能优化 |
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| 引用本文： | 谭文,刘振宇,吴迪,刘相华,王国栋.基于神经元网络和粒子群优化算法的轧制工艺—性能优化[J].宽厚板,2007,13(1). |
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| 作者姓名： | 谭文 刘振宇 吴迪 刘相华 王国栋 |
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| 作者单位： | 东北大学轧制技术及连轧自动化国家重点实验室 |
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| 摘 要： | 为了达到厚板生产中的强度和屈强比等性能指标,本文在用神经元网络对屈服强度和抗拉强度建模的基础上,结合粒子群优化算法对粗轧开轧温度、中间坯厚度、终轧温度、终冷温度及冷却速率等生产工艺参数进行了优化。优化结果与实验室热轧实验及工业试生产结果的对比表明,本模型能有效地优化厚板生产过程的工艺参数,从而为优化工艺或柔性化生产工艺的设计提供指导。
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| 关 键 词： | 厚板 神经网络 粒子群算法 |
Optimization of Rolling Process and Properties Using Artificial Neural Network Combined with Particle Swarm Optimization in Plate Rolling |
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| Tan Wen,Liu Zhenyu,Wu Di,Liu Xianghua,Wang Guodong.Optimization of Rolling Process and Properties Using Artificial Neural Network Combined with Particle Swarm Optimization in Plate Rolling[J].Wide and Heavy Plate,2007,13(1). |
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| Authors: | Tan Wen Liu Zhenyu Wu Di Liu Xianghua Wang Guodong |
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| Affiliation: | The State Key Laboratory of Rolling and Automation of Northeastern University |
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| Abstract: | In the present paper,based on the model of yield strength and tensile strength established by artificial neural network combined with PSO(Particle Swarm Optimization),the optimization of rolling process parameters such as rough rolling start temperature,temperature-holding thickness,finish rolling temperature,finish cooling temperature and cooling rate was carried out in order to obtain the desired strength and yield ratio of heavy plate.The comparison between the results of optimization and the results of hot rolling trials in laboratory and industrial trial production shows that the model is able to effectively optimize the process parameters during heavy plate production,thereby guiding the design of optimized process or more flexible production process. |
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| Keywords: | Heavy plate Artificial neural network PSO |
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