基于改进SVR算法的模具棱线磨损预测方法研究  

Study on Prediction Method of Die Sharp-edged Wear Based on an Improved SVR Algorithm

作　　者：谢晖[1,3] 蒋磊 刘守河 王龙 李乐平 孔繁涛 XIE Hui;JIANG Lei;LIU Shouhe;WANG Long;LI Leping;KONG Fantao(State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body(Hunnan University),Changsha 410082,China;Dongfeng Honda Automobile Co.,Ltd.,New Model Center,Wuhan 430056,China;Jihua Laboratory,Foshan 528200,China;TQM(Hunan)Automotive Technology Co.,Ltd.,Changsha 410082,China)

机构地区：[1]汽车车身先进设计制造国家重点实验室(湖南大学),湖南长沙410082 [2]东风本田汽车有限公司新车型中心,湖北武汉430056 [3]季华实验室,广东佛山528200 [4]湖南天汽模汽车科技有限公司,湖南长沙410082

出　　处：《湖南大学学报（自然科学版）》2024年第8期198-210,共13页Journal of Hunan University:Natural Sciences

基　　金：湖南省2023十大科技攻关项目(2023GK1070);广东省科技重点研发项目(X210181TB210);铝合金车身外覆盖件锐棱成形工艺及其装备产业化研发项目(202N2YCNME001)。

摘　　要：为研究汽车覆盖件模具棱线几何特征参数及成形工艺参数对棱线磨损的影响,实现对模具棱线磨损的精准预测,提出了一种基于改进SVR算法的模具棱线磨损预测模型.通过利用改进的拉丁超立方抽样(ILHS)方法获取模具棱线磨损有限元计算的实验样本,进而构建预测模型的输入参数集.通过耦合混沌理论、动态权重方法对蝗虫优化算法(GOA)进行改进,利用改进后的蝗虫优化算法(IGOA)对SVR算法关键参数进行寻优.构建了基于IGOASVR算法的模具棱线磨损预测模型,结合粒子群寻优算法(PSO)建立多目标优化模型,实现对模具棱线磨损的高精度预测以及几何特征参数和成形工艺参数优化.对比5种常规预测模型,基于IGOA-SVR算法的预测模型在采样点处的预测误差分别为8.546%、8.497%、8.473%,较GOA-SVR预测模型分别提高25.9%、26.2%、26.4%,预测精度相比于其他预测模型也有不同程度的提高.结果表明改进后的IGOA-SVR算法具有更高的精度.To study the influence of geometric characteristic parameters and forming process parameters of automobile stamping die on the sharp-edged wear and realize the accurate prediction of the die sharp-edged wear,a prediction method of the die sharp-edged wear based on improved SVR algorithm was proposed in this paper.By using the improved Latin hypercube sampling(ILHS) method,the experimental samples of finite element calculation of die sharp-edged wear were obtained,and the input parameter set of the prediction model was then constructed.The chaos theory and dynamic weights were introduced into the grasshopper optimization algorithm(GOA),and the improved grasshopper optimization algorithm(IGOA) was used to improve key parameters of the SVR algorithm.Based on the IGOA-SVR algorithm,the prediction model of die sharp-edged wear was constructed,which was combined with the particle swarm optimization(PSO) algorithm to establish a multi-objective optimization model so as to realize the high-precision prediction as well as the optimization of geometric characteristic parameters and forming process parameters.Compared with five existing conventional prediction models,the prediction errors of the prediction model based on IGOA-SVR at the sampling point were 8.546%,8.497%,and 8.473%,respectively,which were 25.9%,26.2%,and 26.4% higher than the GOA-SVR prediction model,respectively,and the prediction accuracy was also improved to varying degrees compared with other prediction models.The results show that the improved IGOA-SVR has higher accuracy.

关 键 词：模具磨损 蝗虫优化算法 支持向量回归 模具锐棱 粒子群寻优算法 

分 类 号：TG76[金属学及工艺—刀具与模具]