An edge-labeling graph neural network method for tool wear condition monitoring using wear image with small samples

Zhi, Gaofeng; He, Dedao; Sun, Weifang; Zhou, Yuqing; Pan, Xiaoming; Gao, Chen

doi:10.1088/1361-6501/abe0d9

Cited by 21 publications

(6 citation statements)

References 27 publications

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“…The network allows for the autonomous and effective extraction of tool degradation features and RUL prediction.In online tool RUL prediction, for a new tool to be tested in cutting conditions, the same multidimensional signals are collected by multiple sensors as in offline training, pre-processed with the same data, and input into the trained model to predict the tool RUL online in real time. According to the ISO3685-1977 standard, the tool wear was defined as the flank wear width VB [40]. Additionally, according to the relevant literature, the tool RUL was considered terminated when the rear face wear value, VB, of the toolexceeds VB max = 0.45 mm.…”

Section: Frameworkmentioning

confidence: 99%

Remaining Useful Life Prediction of Milling Cutters Based on CNN-BiLSTM and Attention Mechanism

Nie

Zhang

et al. 2022

Symmetry

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Machining tools are a critical component in machine manufacturing, the life cycle of which is an asymmetrical process. Extracting and modeling the tool life variation features is very significant for accurately predicting the tool’s remaining useful life (RUL), and it is vital to ensure product reliability. In this study, based on convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM), a tool wear evolution and RUL prediction method by combining CNN-BiLSTM and attention mechanism is proposed. The powerful CNN is applied to directly process the sensor-monitored data and extract local feature information; the BiLSTM neural network is used to adaptively extract temporal features; the attention mechanism can selectively study the important degradation features and extract the tool wear status information. By evaluating the performance and generalization ability of the proposed method under different working conditions, two datasets are applied for experiments, and the proposed method outperforms the traditional method in terms of prediction accuracy.

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Section: Frameworkmentioning

confidence: 99%

Remaining Useful Life Prediction of Milling Cutters Based on CNN-BiLSTM and Attention Mechanism

Nie

Zhang

et al. 2022

Symmetry

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“…Via related intelligent algorithms, the data-driven diagnostic method can adaptively identify equipment operation status information from existing data without the need of prior knowledge for professional technicians 13 . With an edge-labeling graph neural network method, Zhi et al 14 propose a tool for wear condition monitoring using wear images which suitable for small sample conditions. Mishra et al 15 developed a tool condition estimation method during the precision machining process with the unsupervised approach.…”

Section: Introductionmentioning

confidence: 99%

In-situ tool wear condition monitoring during the end milling process based on dynamic mode and abnormal evaluation

Chen,

Mao,

et al. 2024

Sci Rep

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Rapid tool wear conditions during the manufacturing process are crucial for the enhancement of product quality. As an extension of our recent works, in this research, a generic in-situ tool wear condition monitoring during the end milling process based on dynamic mode and abnormal evaluation is proposed. With the engagement of dynamic mode decomposition, the real-time response of the sensing physical quantity during the end milling process can be predicted. Besides, by constructing the graph structure of the time series and calculating the difference between the predicted signal and the real-time signal, the anomaly can be acquired. Meanwhile, the tool wear state during the end milling process can be successfully evaluated. The proposed method is validated in milling tool wear experiments and received positive results (the mean relative error is recorded as 0.0507). The research, therefore, paves a new way to realize the in-situ tool wear condition monitoring.

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“…Since the late 1980s, TCM has been widely studied [12,13]. Since the mechanical equipment is becoming increasingly complicated, the traditional condition monitoring methods based on physical models and signal processing techniques have been less effective in TCM.…”

Section: Introductionmentioning

confidence: 99%

Markov Transition Field Enhanced Deep Domain Adaptation Network for Milling Tool Condition Monitoring

et al. 2022

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Tool condition monitoring (TCM) is of great importance for improving the manufacturing efficiency and surface quality of workpieces. Data-driven machine learning methods are widely used in TCM and have achieved many good results. However, in actual industrial scenes, labeled data are not available in time in the target domain that significantly affect the performance of data-driven methods. To overcome this problem, a new TCM method combining the Markov transition field (MTF) and the deep domain adaptation network (DDAN) is proposed. A few vibration signals collected in the TCM experiments were represented in 2D images through MTF to enrich the features of the raw signals. The transferred ResNet50 was used to extract deep features of these 2D images. DDAN was employed to extract deep domain-invariant features between the source and target domains, in which the maximum mean discrepancy (MMD) is applied to measure the distance between two different distributions. TCM experiments show that the proposed method significantly outperforms the other three benchmark methods and is more robust under varying working conditions.

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An edge-labeling graph neural network method for tool wear condition monitoring using wear image with small samples

Cited by 21 publications

References 27 publications

Remaining Useful Life Prediction of Milling Cutters Based on CNN-BiLSTM and Attention Mechanism

Remaining Useful Life Prediction of Milling Cutters Based on CNN-BiLSTM and Attention Mechanism

In-situ tool wear condition monitoring during the end milling process based on dynamic mode and abnormal evaluation

Markov Transition Field Enhanced Deep Domain Adaptation Network for Milling Tool Condition Monitoring

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