AHPSO-SVM预测超声内圆磨削ZTA陶瓷的边界损伤  

AHPSO-SVM Prediction of Boundary Damage of Ultrasonic Internal Grinding of ZTA Ceramics

作　　者：赵明利[1] 李博涵 聂立新[1] 吕晓峰 赵波[1] ZHAO Mingli;LI Bohan;NIE Lixin;LV Xiaofeng;ZHAO Bo(College of Mechanical & Power Engineering,Henan Polytechnic University,Jiaozuo 454003)

机构地区：[1]河南理工大学机械与动力工程学院,焦作454003

出　　处：《宇航材料工艺》2019年第2期72-76,共5页Aerospace Materials & Technology

基　　金：国家自然科学基金(E51175153);河南理工大学博士基金(B2016-27)

摘　　要：为解决普通加工方式易出现工程陶瓷边缘碎裂的问题,本文对超声内圆磨削工程陶瓷边界损伤预测系统进行了研究。在35 kHz轴向超声磨削与普通磨削两种条件下独立进行试验,运用支持向量机研究工艺参数与边界损伤影响规律,采用改进的粒子群算法优化支持向量机,建立采用混合核函数的AHPSO-SVM预测模型。研究结果表明,超声激励下试件边界损伤降幅为10.05%～21.23%,AHPSO-SVM预测模型MSE为0.378 4、平均相对误差为1.369 0%、30次适应度值标准差为0.020 2。相比于普通磨削,超声磨削可使ZTA陶瓷边界损伤值显著降低;建立的AHPSO-SVM模型具有较好的学习能力、泛化性能与良好的稳定性。In order to solve the problem that there is prone to edge cracking of engineering ceramics with common processing, this paper studies the boundary damage prediction system of ultrasonic internal grinding. The experiment was carried out independently under the conditions of 35 kHz axial ultrasonic grinding and ordinary grinding. The support vector machine was used to study the influence of the process parameters to boundary damage. The improved particle swarm optimization algorithm was used to optimize the support vector machine and the hybrid kernel function AHPSO-SVM predict model was established . The results show that the reduction of boundary damage of the specimen under ultrasonic is 10.05%~21.23%, the MSE of the AHPSO-SVM prediction model is 0.3784, the average relative error is 1.3690%, and the standard deviation of the 30-time fitness value is 0.0202. Compared with ordinary grinding, ultrasonic grinding can significantly reduce the boundary damage value of ZTA ceramics.The established AHPSO-SVM model has better learning ability, generalization performance and good stability.

关 键 词：粒子群算法 支持向量机 超声内圆磨削 ZTA陶瓷 边界损伤 

分 类 号：TG663[金属学及工艺—金属切削加工及机床] TH145.1[一般工业技术—材料科学与工程]