# A Novel Method for Tool Identification and Wear Condition Assessment Based on Multi-Sensor Data
## 4 Tool Wear Assessment
### Result Analysis  
The related principles of Elman_AdaBoost have been introduced. Combined with the above data analysis, each knife contains 315 sets of data, each set of data contains 14 inputs and 3 outputs. In this paper, according to the ratio of 7:3, 215 sets of data were randomly selected as training data and 100 sets of data were used as test data to test the Elman_AdaBoost model. In combination with AdaBoost algorithm, 10 Elman weak classifiers are set up. Each Elman weak classifier is an Elman neural network. The network design structure is as follows:  
1. Input layer design: In pattern recognition, the number of input layer nodes is equal to the number of features of training samples; that is, 14 feature values of input samples are taken as the input of the network, and the number of input nodes is 14.
2. Output layer design: The output node is the three wear states of the tool, that is, the output node is 3.
3. Hidden layer design: The number of hidden layer nodes is calculated preliminarily according to \(k=\sqrt{a+b}+c,c\in[1,10]\) and then the final number of hidden layer nodes is determined by repeated experiments. After repeated experiments, the number of hidden layers is determined to be 20.
4. Bearing layer design: According to the principle of Elman neural network, the number of receiving layers should be equal to the number of hidden layers, so the receiving layer is 20.  
The final Elman network model structure is shown in Figure 11, which is a '7-20-20-3' structure. This paper sets the maximum number of iterations to 500 with an expected error of 0.0001.  
Figure 11: Elman_Adaboost network model structure.  
Taking an Elman network as an example, the training process information is shown in Figure 12a, which shows the gradient value, generalization ability and learning rate of the neural network changing with the number of iterations, and the training target error is shown in Figure 12b.  
This paper uses the first knife as an example to test the nonlinear fitting and classification performance of the Elman_Adaboost model. according to the ratio of 7:3, 215 sets of data were randomly selected as training data and 100 sets of data were used as test data. We get three inputs for any set of inputs, and compare them with the actual output. The error results are shown in Figure 13.  
Figure 12: (**a**) Elman network training process; (**b**) Elman network training target error.  
Figure 13: Predictive and actual output results (**a**) flute1; (**b**) flute2; (**c**) flute3.  
Based on this model, 100 groups of prediction data of three knives were classified into wear period. The results obtained are shown in Table 2 and Figure 14.  
According to the results, the classification accuracy of this model is still high. Considering the commonness of tool wear in engineering practice, this paper cross-verified the classification of three tool wear periods. Taking the 315 sets of data of the first tool as the training data, and the 315 sets of data of the second tool and the third tool as the test data, the classification accuracy was obtained, as shown in Table 3. It can be seen that the accuracy of this model is still relatively high.