基于粗糙集和ACO-LSSVM的Cr12MoV刀具磨损量预测

针对Cr12MoV刀具磨损量预测问题,提出了一种新的粗糙集和最小二乘支持向量机(LSSVM)相结合的预测方法。将声发射信号提取的能量值和切削要素作为预测模型的输入参数,为了降低运算的复杂性,提出采用粗糙集理论对多维输入参数进行降维处理的方法;为提高预测准确性和精度,利用蚁群算法对LSSVM的参数进行优化,建立基于粗糙集和ACO-LSSVM的Cr12MoV刀具磨损量预测模型。仿真结果表明,所建立的Cr12Mo V刀具磨损量预测模型合理有效,具有较强的推广能力和较高的预测精度。

Prediction of Cr12MoV Tool Wear Based on Rough Set and ACO-LSSVM

A new prediction method based on rough set and least square support vector machine (LSSVM) is proposed for the prediction of Cr12MoV tool wear.The acoustic emission signal extraction of energy value and the cutting elements as the input parameters of the prediction model,in order to reduce the computational complexity of the proposed based on rough set theory for multidimensional input parameter method for dimension reduction,in order to improve the prediction accuracy and precision,using ant colony algorithm of LSSVM parameters optimization is established.Finally,the Cr12MoV tool wear prediction model was proposed based on rough set and ACO-LSSVM.The results of simulation show that the Cr12MoV tool wear prediction model is reasonable and effective,and it has strong generalization ability and high prediction accuracy.