摘要
金属的热变形是一个非常复杂的非线性过程,热变形过程中的晶粒尺寸变化直接决定着变形后金属的组织和性能。利用BP神经网络处理了304不锈钢的热变形非线性系统,从试验数据中自动总结出规律。采用人工神经网络技术对304奥氏体不锈钢锻造工艺参数(变形温度和变形速率),再结晶(包括静态再结晶、动态再结晶)和晶粒长大进行建模,分析了静态、动态再结晶晶粒尺寸,并对模型的预测性能进行了研究。
Hot deformation of metals is a very complex nonlinear process, the hot deformation in the process of changes in grain size directly determines the alloy after deformation the organization and performance, BP neural network to deal with 304 stainless steel hot deformation of nonlinear systems, from the experimental data automatically summed up the rule. Artificial neural network technology for 304 austenitic stainless steel parameters of forging process （deformation temperature and strain rate）, recrystallization （including static recrystallization, dynamic recrystallization） and grain growth model to analyze the static and dynamic recrystallization grain size, and forecast the performance of the model were studied.
出处
《山西冶金》
CAS
2009年第1期11-13,28,共4页
Shanxi Metallurgy
基金
山西省自然基金项目(2008012008-2)
关键词
BP神经网络
304不锈钢
锻造
再结晶
晶粒尺寸
BP neural network, 304 stainless steel, forging, recrystallization, grain size
