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机构地区:[1]重庆大学材料科学与工程学院,重庆400044
出 处:《同济大学学报(自然科学版)》2010年第12期1796-1801,共6页Journal of Tongji University:Natural Science
基 金:科技部技术创新基金资助项目(07C26215110824);重庆大学研究生科技创新基金重点资助项目(200811B1B0130302)
摘 要:合理地调节拉深筋阻力可以有效地改善板料拉深的成形质量,为此提出可控拉深筋技术以提高高强度钢板成形性能.以JAC590Y高强度钢板为研究对象,首先通过正交试验设计和数值模拟软件Dyanform相结合,研究三种不同类型的拉深筋运动轨迹对平底盒形件成形性能的影响,以极限拉深深度评判成形性能优劣,确定了优化的拉深筋运动轨迹类型为上升—静止—下降路线,并通过极差分析得到其主要影响因子H1和H2,同时结果表明三类可控拉深筋运动轨迹均能提高高强度钢板的成形性能.然后基于优化的可控拉深筋运动轨迹类型,通过模拟试验数据建立其各个因子与极限拉深深度的GA-BP(遗传算法-反向传播)神经网络预测模型,检验表明该模型能够较好地预测因子对极限拉深深度的影响,预测值与测试值的误差在5%以内.The drawing quality could be improved effectively by a reasonable drawbead restraining force,so the controllable drawbead technology was put forward to improve the formability of high strength steel(HSS).The JAC590Y HSS was selected as an example and three different types of drawbead trajectories to the formability effects were investigated by orthogonal tests and numerical simulation.The optimal type trajectory,ascent-halt-descent,was determined according to the limit drawing depth.The main influence factors H1,H2 were obtained by range analysis and the numerical simulation results indicate that the three types of drawbead trajectories can all improve the formability of HSS.Then the genetic algorithm back propagation(GA-BP)neural network of controllable drawbead trajectory factors-limit drawing depth was constructed by test data of the optimal type trajectory.The estimated values of the limite drawing depth were close to the simulation data and the errors were within 5%
关 键 词:可控拉深筋 盒形件 高强度钢板 数值模拟 遗传神经网络
分 类 号:TG386[金属学及工艺—金属压力加工]
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