轧制差厚板盒形件充液拉深成形缺陷的正交试验分析与神经网络预测  

Orthogonal test analysis and neural network prediction on forming defects in hydroforming for tailor rolled blank box part

作　　者：张华伟 王永喆 吴佳璐 王新刚 Zhang Huawei;Wang Yongzhe;Wu Jialu;Wang Xingang(Guangdong Provincial Key Lab.of Petrochemical Equipment and Fault Diagnosis,College of Mechanical and Electrical Engineering,Guangdong University of Petrochemical Technology,Maoming 525000,China;School of Mechanical Engineering and Automation,Northeastern University,Shenyang 110819,China)

机构地区：[1]广东石油化工学院机电工程学院,广东省石化装备故障诊断重点实验室,广东茂名525000 [2]东北大学机械工程与自动化学院,辽宁沈阳110819

出　　处：《锻压技术》2022年第10期96-102,共7页Forging & Stamping Technology

基　　金：国家自然科学基金资助项目(51475086);广东石油化工学院校级科研基金项目(2020rc020);茂名市科技计划立项项目(2022025)。

摘　　要：对轧制差厚板盒形件的充液拉深成形过程进行了仿真,采用正交试验获取了成形参数对轧制差厚板成形缺陷的影响规律和最优参数组合,并在此基础上建立了BP神经网络模型,对轧制差厚板盒形件的充液拉深成形缺陷进行了预测。结果表明:对于最大厚度减薄率,各因素的影响程度顺序为摩擦因数>厚侧液池压力>厚侧压边力>薄-厚侧液池压力之比>薄-厚侧压边力之比;对于轧制差厚板底部过渡区移动量和法兰处过渡区移动量,各因素的影响程度顺序为厚侧压边力>薄-厚侧压边力之比>厚侧液池压力>薄-厚侧液池压力之比>摩擦因数。综合考虑厚度减薄率和过渡区移动情况,得到最优参数组合为厚侧压边力为20 kN、薄-厚侧压边力之比为1.5、厚侧液池压力为0.5 MPa、薄-厚侧液池压力之比为2.0、摩擦因数为0.200。基于正交试验分析结果建立的BP神经网络模型能够实现对轧制差厚板盒形件充液拉深成形缺陷的准确预测。The hydroforming process of tailor rolled blank(TRB)box part was simulated,and the influencing laws of forming parameters on the forming defects of TRB and the optimal parameters combination were obtained by the orthogonal test.Then,on this basis,the BP neural network model was established to predict the forming defects of TRB box part in hydroforming.The results indicate that for the max-imum thinning rate,the order of the influence degree of each factor is friction coefficient>liquid pressure on thicker side>blank holder force(BHF)on thicker side>ratio of liquid pressure on thinner side to thicker side>ratio of BHF on thinner side to thicker side.For the movement of transition area at the bottom of TRB and the movement of transition area at the flange,the order of the influence degree of each factor is BHF on thicker side>ratio of BHF on thinner side to thicker side>liquid pressure on thicker side>ratio of liquid pressure on thinner side to thicker side>friction coefficient.Taking into account the thinning rate and the movement of transition area,the optimal pa-rameters combination is the BHF on thicker side of 20 kN,the ratio of BHF on thinner side to thicker side of 1.5,the liquid pressure on thicker side of 0.5 MPa,the ratio of liquid pressure on thinner side to thicker side of 2.0,and the friction coefficient of 0.200.Thus,the BP neural network model based on the orthogonal test analysis results can precisely predict the forming defects of TRB box part in hydro-forming.

关 键 词：轧制差厚板 充液拉深 盒形件 厚度减薄 过渡区移动 神经网络模型 

分 类 号：TG386.3[金属学及工艺—金属压力加工]