Tool Wear State Identification and Prediction Method Based on Improved EfficientNetV2 and UNetTSF

doi:10.3969/j.issn.1004-132X.2026.03.016

Abstract

Abstract:

In order to improve the accuracy of tool wear prediction for the problems of tool in-machine wear condition monitoring， a new monitoring model named GAF-iEfficientNetV2-UNetTSF was proposed integrating GAF， the improved EfficientNetV2 lightweight network， and the UNetTSF time-series prediction model. The model adopted the strategy of first classification and then prediction. Firstly， the force signals were acquired during machining processes by tool， and the feature dimensionality reduction was realized by segmented aggregation technique. Then GAF was used to encode the three-directional force signals respectively， and three groups of single-channel images were obtained. The three groups of single-channel images under the same time sequence were stacked into three-channel images. Subsequently， an improved EfficientNetV2 training network was constructed to automatically extract and classify features to recognize the tool wear states. Finally， for the most critical tool wear states， the UNetTSF model was utilized for wear value prediction in order to achieve accurate prediction. Through comparative experiments， the high accuracy of the model in the task of tool wear state recognition and the high precision in wear value prediction were verified. The results provide an efficient and accurate monitoring method in the field of tool wear state monitoring， and is of great significance for improving industrial production efficiency and reducing maintenance costs.

Key words: Gramian angular field （GAF）, EfficientNetV2, UNetTSF, tool wear state, tool wear monitoring

摘要：

针对刀具在机磨损状态监测问题，为提高刀具磨损预测的准确性，提出了一种融合格拉姆角场（GAF）、改进的EfficientNetV2轻量级网络和UNetTSF时序预测模型的新型监测模型GAF-iEfficientNetV2-UNetTSF。该模型采用先分类后预测的策略，采集刀具加工过程中的力信号，并使用分段聚合技术实现特征降维，利用格拉姆角场分别对三向力信号进行编码，得到三组单通道图像，并将同一时序下的三组单通道图像堆叠成三通道图像。构建改进的EfficientNetV2训练网络进行特征的自动提取和分类以实现刀具磨损状态识别。针对最关键的刀具磨损状态，利用UNetTSF模型进行磨损值预测，以实现精确预判。通过对比实验，验证了该模型在刀具磨损状态识别任务中的高准确率以及磨损值预测方面的高精度，为刀具磨损状态监测领域提供了一种更精准的监测方法，对提高工业生产效率和降低维护成本具有重要意义。

关键词: 格拉姆角场, EfficientNetV2, UNetTSF, 刀具磨损状态, 刀具磨损监测

CLC Number:

TH165

ZHANG Xi, ZHU Hong, ZHANG Longjia, ABDELTAWAB Ahmed. Tool Wear State Identification and Prediction Method Based on Improved EfficientNetV2 and UNetTSF[J]. China Mechanical Engineering, 2026, 37(3): 668-678.

张曦, 朱红, 张龙佳, ABDELTAWAB Ahmed. 基于改进的EfficientNetV2和UNetTSF的刀具磨损状态识别及预测方法[J]. 中国机械工程, 2026, 37(3): 668-678.

Figures/Tables 25

Fig.1 GAF plot of the force signal in the x-axis direction for a certain time series

Fig.2 MBConv module structure

Fig.3 Fused MBConv module structure

Fig.4 FPN model

Fig.5 UNetTSF model structure

Fig.6 GAF-iEfficientNetV2 structure

Fig.7 SCConv module structure

Fig.8 GAM module structure

Fig.9 Structure of tool wear state monitoring model based on GAF-iEfficientNetV2-UNetTSF

Tab.1 Cutting parameters

参数	数值
主轴转速/（r·min^-1）	10 400
进给速率/（mm·min^-1）	1555
径向切深/mm	0.125
轴向切深/mm	0.2
采样频率/Hz	50 000

Fig.10 Results of Spearman's correlation analysis between Vb value and input signal

Fig.11 C1 milling cutter back face wear variation curve

Fig.12 Model training effects

Fig.13 Improved model accuracy validation curves

Tab.2 Comparison of model performance before and after improvement

模型	准确率	精确率	召回率	F₁
EfficientNetV2	95.30	95.31	95.30	95.31
SC-EfficientNetV2	96.21	96.24	96.21	96.23
G-EfficientNetV2	96.50	96.51	96.50	96.51
iEfficientNetV2	97.95	97.96	97.95	97.96

Fig.14 Confusion matrix of EfficientNetV2 on the test set

Fig.15 Confusion matrix of iEfficientNetV2 on the test set

Fig.16 Stable wear phase model prediction effects

Fig.17 Accelerated wear phase model prediction effects

Fig.18 Improved model accuracy validation curves

Tab.3 Comparison of the performance of each model

模型	准确率	精确率	召回率	F₁
GAF-ResNet18	95.09	95.08	95.09	95.09
GAF-VGG16	95.77	95.76	95.77	95.77
GAF-ShuffleNetV2	94.41	94.46	94.41	94.44
GAF-MobileNetV2	95.60	95.59	95.60	95.60
GAF-iEfficientNetV2	97.95	97.96	97.95	97.96

Fig.19 Predictive results of contrast models during stable wear stage

Fig.20 Predictive results of contrast models during accelerated wear stage

Tab.4 Comparison of the performance of each model in stable wear stage

模型	RMSE	MAE	MAPE/%	R²
BiLSTM	1.50	0.90	5.63	0.93
TCN	1.57	1.02	5.69	0.92
SVR	1.63	1.05	5.68	0.91
UNetTSF	1.26	0.54	5.58	0.95

Tab.4 Comparison of the performance of each model in accelerated wear stage

模型	RMSE	MAE	MAPE/%	R²
BiLSTM	1.47	0.96	6.09	0.95
TCN	1.82	1.20	6.05	0.93
SVR	1.95	1.42	6.12	0.95
UNetTSF	1.25	0.54	6.10	0.95

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