锻压技术

基于K-means聚类算法的比例加载路径的优化

英文标题：Optimization on proportional loading path based on K-means clustering algorithm

作者：张坤岩杨连发陈占斌宗鹏举

单位：桂林电子科技大学

分类号：TG304

出版年,卷(期):页码：2021,46(11):183-189

摘要：

为了探索工艺参数对管材液压成形工艺的影响，研究了比例加载。通过理论计算和仿真结合的方法，对TP2铜管进行了管材液压成形，并且对比分析了试验与仿真环境下胀形区的轴向轮廓，以相对误差小于10%作为判断准则，对模型进行验证。通过改变液压力与轴向进给量,观察并分析了环向与轴向流动应力分量的变化，最后采用Kmeans聚类算法进行优化，实现了比例加载，并得到了该加载路径，其在胀形过程中的应力分量比值R=2.233，误差范围为：-1.46%≤Ei≤1.6%。结果表明，Kmeans聚类算法优化后的流动应力分量比值的波动范围明显减小，说明该算法可以优化加载路径。

In order to explore the influences of process parameters on hydroforming process of tube, the proportional loading was studied. Then, the hydroforming of TP2 copper pipe was conducted by the combining of theoretical calculation and simulation, and the model was verified by comparing and analyzing the axial profile of the forming zone in the experiment and simulation environment and taking the relative error less than 10% as the judgment criterion. Furthermore, the change of flow stress component was observed and analyzed by changing the hydraulic pressure and axial feeding amount. Finally, the proportional loading was realized by optimization of K-mean clustering, and the loading path was obtained. During the bulging process, the stress component ratio was R=2.233, and the error range was -1.46%≤Ei≤1.6%. The results show that the fluctuation range of flow stress component ratio after optimization of K-means clustering is significantly reduced, indicating that the algorithm can optimize the loading path.

基金项目：

广西研究生教育创新计划项目（JGY2019074）；国家自然科学基金资助项目(52065014)；广西自然科学基金资助项目(2017GXNSFAA198133)；桂林电子科技大学研究生优秀学位论文培育项目(18YJPYSS03)

作者简介：张坤岩（1993-），男，硕士研究生，E-mail：z_kunyan@163.com；通信作者：杨连发（1965-），男，博士，教授，E-mail：y-lianfa@163.com

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