Python code data of attention-based dual-scale hierarchical LSTM for tool wear monitoring

The experiment is based on the common high speed milling data set to verify the robustness of the model to various tool types. The data set contains six sub data sets, corresponding to the wear process of six different types of tools. Three of the sub data sets contain tool wear labels, while the other three sub data sets do not. The tools used are all three edged 6mm ball cemented carbide tools, but their geometry and coating are different. The workpiece is Inconel 718, which is widely used for jet engine blade milling. The spindle speed is 10360rpm, and the cutting depth is 0.25mm. The tool cuts from the upper edge of the workpiece surface to the lower edge in a zigzag manner. In the whole milling process, the cutting length of each tool is about 0.1125m × 315pass = 35.44m. The cutting signal in Experiment 1 includes the cutting force signal collected by the three channel Kistler dynamometer and the vibration signal collected by the three channel Kistler accelerometer at a sampling rate of 50 kHz. Use the microscope LEICA MZ12 to measure the wear of the rear tool surface of the three teeth offline after each tool feeding. In this experiment, a cutting signal is collected every other period of time to predict the wear of the three teeth of the tool.The samples are divided into training set, evaluation set, test set and reconstruction set. The training set and evaluation set samples are from two kinds of tools, including 30000 and 4096 samples respectively; The samples of the test set are from another tool, including 9472 samples; The reconstruction set comes from the unlabeled data generated by the other three tools, including 40832 samples. Each sample contains three channels of cutting force signal and three channels of vibration signal. The sampling points of each channel signal are 2304. The following preprocessing steps are performed:1) Signal clippingSince the feed rate and sampling rate are constant throughout the experiment, the data set of each experiment can be approximately understood as a signal matrix evenly distributed on the workpiece surface, ignoring the slight difference in the number of sampling points for each tool path. The ordinate of the matrix corresponds to the index of the tool path times, and the abscissa corresponds to the index of the sampling point. Because the generation rules of cutting signals are different in uncut, cut in, cut out and stable states, the sampling points close to the edge of the workpiece are removed. Here we simply cut 2% off the two ends of the cutting signal obtained by each tool feed.2) Data amplificationBecause tool wear can only be observed with a microscope after each tool feeding, each wear tag corresponds to a cutting signal containing about 120000 sampling points, and the acquisition of tool wear also takes a lot of time. In this case, the number of tags extracted is not enough to fit the model, nor can the robustness of the algorithm be guaranteed. It is necessary to artificially split the sample and expand the tool wear label. Considering that the tool wear is a slow and continuous process, and there is a certain deviation in the experimental measurement, the linear interpolation method is adopted here. We also tested quadratic interpolation and polynomial fitting methods, but no better results were observed. It needs to be stated here that the essence of prediction is to find a function that maps the sample space to the target space. For any point in the sample space, the model can find the corresponding value in the target space. What sample amplification does is to sample more times in the target space, so as to more comprehensively describe this mapping relationship, rather than redefining this relationship.The task of this study is to monitor the wear of the rear cutter surface of the three teeth according to the six channel sensor signals. On the test set, the mean square error (MSE) and mean absolute percentage error (MAPE) between the predicted value and the observed value of the microscope are 0.0013 and 4%, respectively, and the average and maximum final prediction error (FPE) are 5 μ M and 23 μ m. The training time was 2130s, and the single prediction time was 1.79ms. The accuracy, training time and detection efficiency of tool wear monitoring can meet the current industrial needs. As MPAN realizes the mapping from cutting signal to tool wear, as the gate of control information flow, attention unit retains the importance information of input features. The predicted tool wear curve is basically consistent with the curve observed by the microscope.