基于粒子群优化算法-长短时记忆模型的刀具磨损预测方法

Tool wear prediction method based on particle swarm optimizationlong and short time memory model

为确保车削加工的表面质量和加工稳定性,实现对车刀磨损状态的实时准确监控,提出了基于小波阈值去噪、长短时记忆(LSTM)网络和粒子群优化算法(PSO)的刀具磨损状态预测模型。采用改进多项式阈值函数对刀具加速度振动信号进行去噪,构建了优质的信号输入样本。训练长短时记忆网络对刀具后刀面磨损值进行预测和磨损状态分类。利用粒子群优化算法对网络进行参数寻优,结果表明,提出的PSO-LSTM模型在预测和分类精度方面均优于未优化的LSTM网络。

In order to ensure the surface quality and machining stability of turning,the real-time and accurate monitoring of turning tool wear state is realized.A tool wear state prediction model based on wavelet threshold denoising,Long and Short Time Memory(LSTM)network and Particle Swarm Optimization(PSO)was proposed.The improved polynomial threshold function was used to denoise the tool acceleration vibration signal,and the high quality signal input sample was constructed.The wear values of the tool rear face were predicted and the wear states were classified by training the LSTM network.The proposed PSO-LSTM model is superior to the unoptimized LSTM network in terms of prediction and classification accuracy by using PSO.