计量学报2026，Vol.47Issue(3)：324-333,10.DOI:10.3969/j.issn.1000-1158.2026.03.02
基于TTAO优化算法优化VMD和RF算法的刀具磨损预测研究
Tool Wear Prediction Study Based on TTAO Algorithm Optimized VMD and RF Algorithm
摘要
Abstract
Measuring and accurately predicting the wear of milling tools is an important means of improving processing efficiency and reducing production costs.To improve the accuracy of tool wear prediction,the triangulation topology aggregation optimizer(TTAO)algorithm is used to optimize the variational modal decomposition(VMD)decomposition parameters K and α of cutting force signals at different tool wear stages.This is combined with a random forest(RF)algorithm to predict tool wear values.First,different wear stages are classified based on the wear mechanism of cutting tools,and VMD decomposition is performed on cutting force signals from different periods.Then,the TTAO algorithm is used for parameter optimization to improve signal decomposition accuracy.Secondly,the optimized VMD algorithm is combined with permutation entropy to denoise cutting force data from different wear stages,and wear features are extracted to construct a wear prediction training model for tool wear prediction.Finally,the predicted matrix samples are input into the random forest algorithm for tool wear identification.The results show that targeted noise reduction of cutting force data at different wear stages can effectively reduce the error in tool wear prediction,providing a new method for improving the accuracy of tool wear prediction,the error assessment metrics show a 33.14%reduction in mean absolute percentage error(eMAPE),a 39.46%decrease in mean absolute error(eMAE)and a 40.07%decline in root mean square error(eRMSE).关键词
几何量计量/刀具磨损预测/三角拓扑聚合优化/变分模态分解/随机森林/误差评估Key words
geometrial metrology/tool wear prediction/triangulation topology aggregation optimizer/variational modal decomposition/random forest/error assessment分类
通用工业技术引用本文复制引用
郭淼现,周亮,江小辉,黄之文,龚多甫..基于TTAO优化算法优化VMD和RF算法的刀具磨损预测研究[J].计量学报,2026,47(3):324-333,10.基金项目
国家自然科学基金面上项目(52275452) （52275452）
上海航天科创基金(SAST2023-063) （SAST2023-063）
