锻压技术

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

英文标题：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

单位：广东石油化工学院东北大学

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

出版年,卷(期):页码：2022,47(10):96-102

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 maximum 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 parameters 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 hydroforming.

基金项目：

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）

张华伟(1983-)，男，博士，副教授,E-mail：zhanghw@neuq.edu.cn

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