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作 者:蔡鸣 朱光[2,3] 李论[2,3] 赵吉宾[2,3] 王奔 CAI Ming;ZHU Guang;LI Lun;ZHAO Jibin;WANG Ben(School of Mechatronics Engineering,Shenyang Aerospace University,Shenyang 110136,China;Shenyang Institute of Automation,Chinese Academy of Sciences,Shenyang 110016,China;不详)
机构地区:[1]沈阳航空航天大学机电工程学院,沈阳110136 [2]中国科学院沈阳自动化研究所,沈阳110016 [3]中国科学院机器人与智能制造创新研究院,沈阳110169
出 处:《组合机床与自动化加工技术》2024年第1期174-177,182,共5页Modular Machine Tool & Automatic Manufacturing Technique
基 金:国家自然科学基金项目—辽宁省联合基金项目(U1908230);辽宁省自然科学基金计划面上项目(2023-MS-034)。
摘 要:为了建立磨抛工艺参数与材料去除深度的关系,建立一种基于最小二乘法支持向量回归机(LSSVR)的材料去除深度预测模型,并引入粒子群优化(PSO)算法来优化LSSVR的超参数,可提高LSSVR模型的预测准确性和全局优寻能力。搭建叶片机器人砂带磨抛实验平台,设计并进行多工艺参数实验,考虑工艺参数:砂带粒度、砂带转速、进给速度、接触力和叶片表面曲率半径,获得叶片表面的材料去除深度,最终利用实验数据建立了PSO-LSSVR叶片材料去除深度预测模型。结果表明,PSO-LSSVR模型的预测准确率为95.37%,平均预测误差为0.003463,说明PSO-LSSVR模型具有较高的预测精度,并结合实际加工情况进行实验验证可行性,证明PSO-LSSVR模型可以有效合理地建立工艺参数与材料去除深度的关系。In order to establish the relationship between grinding process parameters and material removal depth,a material removal depth prediction model based on least square support vector regression(LSSVR)was first established,and particle swarm optimization(PSO)algorithm was introduced to optimize the LSSVR hyperparameters,which can improve the prediction accuracy and global optimization ability of the LSSVR model.The experimental platform of robot blade abrasive belt polishing was built,and multi-process parameter experiments were designed and carried out.The material removal depth of blade surface was obtained by considering process parameters such as sand belt particle size,sand belt speed,feed speed,contact force and blade surface curvature radius.Finally,the prediction model of PSO-LSSVR blade material removal depth was established by using experimental data.The results show that:The prediction accuracy of the PSO-LSSVR model was 95.37%,and the average prediction error was 0.003463,indicating that the PSO-LSSVR model had a high prediction accuracy.The feasibility was verified by experiments combined with the actual processing situation,which proved that the PSO-LSSVR model could effectively and reasonably establish the relationship between process parameters and material removal depth.
关 键 词:机器人砂带磨抛 预测模型 工艺参数 最小二乘法支持向量回归机 粒子群算法
分 类 号:TH161[机械工程—机械制造及自动化] TG580[金属学及工艺—金属切削加工及机床]
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