A Dual-Stage Attention Model for Tool Wear Prediction in Dry Milling Operation

Abstract

The intelligent monitoring of tool wear status and wear prediction are important factors affecting the intelligent development of the modern machinery industry. Many scholars have used deep learning methods to achieve certain results in tool wear prediction. However, due to the instability and variability of the signal data, some neural network models may have gradient decay between layers. Most methods mainly focus on feature selection of the input data but ignore the influence degree of different features to tool wear. In order to solve these problems, this paper proposes a dual-stage attention model for tool wear prediction. A CNN-BiGRU-attention network model is designed, which introduces the self-attention to extract deep features and embody more important features. The IndyLSTM is used to construct a stable network to solve the gradient decay problem between layers. Moreover, the attention mechanism is added to the network to obtain the important information of output sequence, which can improve the accuracy of the prediction. Experimental study is carried out for tool wear prediction in a dry milling operation to demonstrate the viability of this method. Through the experimental comparison and analysis with regression prediction evaluation indexes, it proves the proposed method can effectively characterize the degree of tool wear, reduce the prediction errors, and achieve good prediction results.

A hybrid tool wear prediction model based on JDA

“…The specific results are shown in Figure 10. The accuracy indicators are shown in Table 7, and the calculation formula for the accuracy indicators is calculated by formula (8 ∼10) (Qin et al. , 2022).…”

Theory based on HMM and Bayesian analysis for predicting the remaining useful life of tools

“…The method proposed in this paper was validated using the publicly available dataset from the PHM2010 data challenge, a source frequently referenced by scholars [9][10][11][12] . In the process of cutting, data from seven distinct sources were gathered, including signals of cutting force in three axes, acceleration signals across three axes, and signals of acoustic emission, each sampled at a frequency of 50 kHz.…”

Identification of Milling Cutter Wear State under Variable Working Conditions Based on Optimized SDP

“…Huang et al [14] have used reconstructed time series layers to represent multi-sensor original signals and employed convolutional neural networks for automatic recognition of tool wear. Data-driven recognition methods do not require precise analytical models or extensive domain expertise and reasoning mechanisms, but they typically need a large amount of training data, and the training and testing data must be independently and identically distributed [15,16]. However, the actual cutting environment is harsh and variable, with data distribution differences under different conditions, making traditional data-driven tool wear recognition methods unsuitable for some conditions with scarce data or a lack of labeled data [17].…”