AO-LSSVM在铣削铝合金表面粗糙度预测研究与应用

Research and Application of AO-LSSVM in Milling Aluminum Alloy Surface Roughness Prediction

为提高铣削7475铝合金表面粗糙度(Ra)的预测准确性和便捷性,本文基于天鹰优化器算法(AO)对最小二乘向量机(LSSVM)进行优化,以4个铣削参数作为输入值,R_(a)作为输出值构建铣削铝合金R_(a)预测模型,通过与粒子群(PSO)优化最小二乘支持向量机(LSSVM)和LSSVM两种算法进行对比,采用灰色关联对铣削参数与表面粗糙度之间的相关性进行分析并通过GUI界面搭建Ra预测系统。结果表明:基于AO LSSVM的Ra预测模型的预测误差为4.2876%,拟合优度达到0.93864,优于其他算法;每齿进给量与Ra的相关性最大,灰色关联度值为0.764;通过GUI预测应用系统能实现高效、便捷、准确地预测R_(a)值。

To further improve the accuracy and convenience of prediction on the surface roughness(Ra)of milled 7475 aluminum alloy,the least squares vector machine(LSSVM)was optimized based on the Aquila Optimizer algorithm(AO).The Ra prediction model of milled aluminum alloy was constructed with four milling parameters as the input values and Ra as the output value.This model was compared with two other algorithms,PSO-LSSVM and LSSVM.The correlation between the milling parameters and surface roughness was analyzed using gray correlation.The Ra prediction system was built through the GUI interface.The results show that the prediction error of the Ra prediction model based on AO-LSSVM is 4.2876%,and the goodness-of-fit reaches 0.93864,which is better than other algorithms.The correlation between feed per tooth and Ra is the largest,with a gray correlation value of 0.764.The GUI prediction application system can realize efficient,convenient,and accurate prediction of Ra values.