AMPSO与SVR相结合的铣刀寿命预测研究  

Life Prediction of Milling Cutters Combining AMPSO with SVR

作　　者：罗丹 惠记庄[1,2] 丁凯[1,2] 张泽宇 邵树军 杜超[3] LUO Dan;HUI Jizhuang;DING Kai;ZHANG Zeyu;SHAO Shujun;DU Chao(Institute of Smart Manufacturing Systems,School of Construction Machinery,Chang'an University,Xi'an 710064,China;The Ministry of Education Key Laboratory of Road Construction Technology and Equipment,Chang'an University,Xi'an 710064,China;Shaanxi Fast Gear Co.,Ltd.,Xi'an 710119,China)

机构地区：[1]长安大学工程机械学院智能制造系统研究所,西安710064 [2]长安大学道路施工技术与装备教育部重点实验室,西安710064 [3]陕西法士特齿轮有限责任公司,西安710119

出　　处：《机械科学与技术》2023年第5期730-735,共6页Mechanical Science and Technology for Aerospace Engineering

基　　金：陕西省科技重大专项(2018zdzx01-01-01);中央高校基本科研业务费高新技术培育项目(300102250201);陕西省高等教育教学改革重点攻关项目(19BG010)。

摘　　要：针对支持向量回归机在预测铣刀寿命时惩罚参数和核函数参数难确定、不同的参数设置对预测效果影响较大的问题,提出了自适应变异粒子群算法。在支持向量回归算法的基础上,引入AMPSO优化SVR参数,建立AMPSO与SVR相结合的数控铣刀寿命预测模型。通过硬质合金钢铣刀铣削的实验验证表明,相比于网格搜索法和神经网络算法,AMPSO-SVR算法在测试样本集的平均相对预测误差低至0.72%,相较前两者预测误差更小,可准确预测数控铣刀寿命,为数控加工过程中的换刀决策提供依据。Aiming at the problems that it is difficult to determine the penalty parameter and kernel function parameter,and different parameters have a great influence on the prediction effect with the support vector regression to predict the milling cutter’s life,the adaptive mutation particle swarm optimizationalgorithm was proposed.On the basis of the support vector regression algorithm,AMPSO(Adaptive Mutation Particle Swarm Optimization)was used to optimize the SVR(Support Vector Regression)parameters,and a life prediction model for milling cutter combining AMPSO with SVR was established.The results show that the average relative prediction error of the AMPSO-SVR algorithm in the test sample set is as low as 0.72%,which is smaller than that of the grid search method and the neural network algorithm.It can accurately predict the milling cutter’s life,which can provide a basis for tool change decisions during the machining process.

关 键 词：铣刀 支持向量回归机 自适应变异粒子群算法 寿命预测 

分 类 号：TG71[金属学及工艺—刀具与模具] TP183[自动化与计算机技术—控制理论与控制工程]