Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (4): 989-997.doi: 10.13229/j.cnki.jdxbgxb.20210778

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

Fei WU(),Hao-ye NONG,Chen-hao MA   

  1. School of Mechanical and Electrical Engineering，Wuhan University of Technology，Wuhan 430070，China
  • Received:2021-08-12 Online:2023-04-01 Published:2023-04-20

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Abstract:

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.

Key words: mechanical manufacturing and automation, turning, tool wear, condition monitoring, deep learning, long and short time memory network

CLC Number: 

  • TP183

Fig.1

Metallograph of worn flank surface"

Fig.2

Sensor arrangement of CNC lathe"

Table 1

Combinations of cutting parameters"

主轴转速/（r·min-1）进给量/（mm·min-1）背吃刀量/mm
8501000.4
8501500.5
8502000.6
9501000.5
9501500.6
9502000.4
11501000.6
11501500.4
11502000.5

Table 2

Tool wear stages"

状态后刀面磨损/mm定制后刀面磨损值/mm
初期磨损0.00~0.100， 0.05
正常磨损0.10~0.250.10， 0.15， 0.20
剧烈磨损0.25~0.300.25
磨钝失效≥0.300.30

Fig.3

Wavelet threshold denoising"

Fig.4

Function image of improved threshold function at λ=1"

Fig.5

LSTM cell structure"

Fig.6

Comparison of signal denoising results"

Table 3

Initial parameters of the PSO-LSTM model"

网络参数初始值
初始化权重［-0.5， 0.5］
遗忘门偏置量1或2
输入门偏置量［0， 0.8］
输出门偏置量［0， 0.8］
迭代次数100
种群规模50
学习因子c11.5
学习因子c21.7
隐藏层单元数m100
学习率r0.01

Fig.7

Network structure of LSTM"

Fig.8

PSO optimization results"

Fig.9

Prediction results and MAPE of PSO model and PSO-LSTM model"

Table 4

Evaluations of prediction results of two models"

识别方法MSERMSEMAPE
LSTM2.81221.67690.0111
PSO?LSTM0.89930.94830.0059

Table 5

Recognition accuracy of model before andafter optimization"

模型初期正常剧烈磨钝总体
LSTM90.094.093.387.592.1
PSO?LSTM96.796.096.793.896.0
提升6.72.03.46.33.9
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