摘要
为解决在机械加工过程中刀具的磨损及崩刃对加工质量和效率的影响,通过机器人学习技术,设计一套基于EMD-PSO-HMM刀具磨损监控系统。首先提取不同刀具磨损状态下主轴的电流信号,由于传统小波分析及傅里叶分析在信号分析过程存在一定局限性,文章采用EMD算法对加工过程中主轴电流信号进行不同尺度信号分解并提取特征参数,将提取的特征值输入HMM模型进行训练迭代。为解决HMM模型在模型训练的过程中存在局部最小值的问题,文章引入粒子群算法对HMM模型的输入参数进行全局搜索以达到最优值。基于以上形成的EMD-PSO-HMM刀具磨损监控系统在实际刀具磨损状态评估过程中具有较高的准确性。
In order to solve the influence of tool wear and edge collapse on machining quality and efficiency during machining,a tool wear monitoring system based on EMD-PSO-HMM through robot learning technology is designed.Firstly,the Spindle current signal under different tool wear states is extracted.Due to the limitations of wavelet analysis and Fourier analysis in the signal analysis process,The EMD algorithm is used to decompose the spindle current signals at different scales during the machining process and extract feature parameters.The extracted feature values are entered into the HMM model for training and iteration.In order to solve the problem of local minimum values in the HMM model training process,Particle swarm optimization algorithm is introduced in this paper introduces to globally search for the input parameters of the HMM model,so it can achieve the optimal value.The tool wear monitoring system based on EMD-PSO-HMM have high accuracy in the actual tool wear status evaluation.
作者
张子盛
孙爱民
赖智宇
方旭阳
ZHANG Zisheng;SUN Aimin;LAI Zhiyu;FANG Xuyang(Liuzhou Saike Technology Development Co.,Ltd.,Liuzhou 545000,CHN;State Key Laboratory for High Performance Tools,Zhengzhou 450007,CHN)
出处
《制造技术与机床》
北大核心
2024年第5期139-144,共6页
Manufacturing Technology & Machine Tool
