基于改进的EfficientNetV2和UNetTSF的刀具磨损状态识别及预测方法

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关键词：格拉姆角场;EfficientNetV2;UNetTSF；刀具磨损状态；刀具磨损监测中图分类号：TH165DOI：10.3969/j.issn.1004-132X.2026.03.016 开放科学（资源服务）标识码（OSID）：

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-iEficientNetV2-UNetTSF was proposed integrating GAF，the improved EficientNetV2 lightweight network，and the UNetTSF time-series prediction model. The model adopted the strategy offirstclassification 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 ofsingle-channel images under the same time sequence were stacked into three-channel images.Subsequently，an improved EffcientNetV2 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 highaccuracy 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）； EficientNetV2；UNetTSF；tool wear state； tool wearmonitoring

0 引言

随着数控加工技术的不断发展，刀具作为加工的关键要素之一，其性能直接影响生产效率和产品质量。（剩余16265字）