摘要
寻求微观组织与性能的定量关系一直是研究开发离线与在线预测系统的关键问题 ,针对热轧带钢SS40 0性能预测系统 ,提出了基于Matlab神经网络工具箱的神经网络解决方案 .该模型采用前向神经网络 ,在利用BP算法的基础上 ,为了克服常规BP学习算法的缺陷 ,Matlab神经网络工具箱对常规BP算法进行了改进 ,采用更有效的数值优化方法 ,如Levenberg Marquardt优化方法 ,建立了化学成分和生产的主要工艺参数与产品力学性能之间的关系 .结果表明影响板带屈服强度。
Seeking quantitative relation between the microstructure and properties is a key point for on-line and off-line predictive system in hot rolling process. In order to develop a system for predicting properties of hot rolling strip steel SS400 during hot rolling process, a model of neural network model used in predicting properties of hot rolling strip is developed based on Matlab neural network toolbox. By using a back propagation (BP) algorithm, to eliminate the shortcoming of general back-propagation algorithm, Matlab neural network toolbox has modified BP algorithms. Using more effective numerical optimization algorithms, such as Levenberg-Marquardt, the relation between chemical composition and main hot rolling parameter and mechanical properties of product has been built in this prediction model. The analysis indicates that the model is not only simpler in programming, but also quicker in convergence. It is concluded that the main factors influencing the yield strength, tensile strength and elongation of steel are strip thickness and carbon content.
出处
《沈阳工业学院学报》
2004年第2期8-10,26,共4页
Journal of Shenyang Institute of Technology
