## 4 Results and Discussion
### ANFIS in Predicting MRD
#### 4.2.1 ANFIS Rules and Membership Function  
ANFIS utilizes the neural network's capacity for learning to implement the three fundamental processes of fuzzy control, fuzzy reasoning, and defuzzification. The self-adaptive tuning of the ANFIS system changes the membership function of each parameter of the model, and the optimal parameters of the membership function can be effectively calculated through self-learning, so that the designed fuzzy reasoning system can best simulate the desired input-output relations. Finding the fuzzy controller's input and output variables is the first stage in the ANFIS system. Speed, force, and mesh were the three input parameters for the ANFIS model discussed in this article. The output was the blade's material clearance depth (MRD). An ANFIS is a multiple-input single-output (MISO) system, as explained in this paper.  
In this study, the Generalized Bell-shaped membership function was used to design 125 fuzzy rules for MRD prediction in the topology diagram of ANFIS, as shown in Figure 10. Three feature sets were used as inputs to build fifteen membership functions. For the purpose of fuzzifying the input features, each feature produced five member functions. The second level displays the 15 component functions in question. MF 1-1,..., and MF 1-5 were the membership functions for the input feature speed. The membership functions of the input feature Force were MF 2-1,..., and MF 2-5. The input feature Mesh had membership functions MF 3-1,..., and MF 3-5. The 225 data points in the sample used for this study were split between training and testing, with 70% of the data being used for training and 30% for testing. The \"andMethod\" and \"orMethod\" functions used in this paper to determine the second layer's emission intensity were \"Prod\" and \"Max\". By dividing the incoming signals, this function determined the emission intensity of each rule. For layers 3-5, \"wtaver\", \"prod\", and \"max\" were chosen as the appropriate \"defuzz method\", \"imp method\", and \"agg method\" in this work, respectively. The Generalized Bell-shaped membership function was chosen as the membership function. It was decided to use the least squares estimation-gradient descent method for the overall learning rule algorithm. The ANFIS parameter is shown in Table 5.  
Figure 10: Topological structure of ANFIS.  
Figure 11 shows the membership function images of three grinding input parameters obtained by training the generalized bell-shaped membership function before and after training. The membership function is a function used to describe the degree to which a variable belongs to a fuzzy set, in which the possible value range is \((0,1)\). The membership function used in this paper was generalized bell-shaped membership (Gbellmf). The three membership functions with grinding input parameters obtained by training the Gbellmf can be observed.  
It can be seen from Figure 11 that the shape of the membership function of the input parameters changed significantly before and after training. By analyzing the generalized bell-shaped membership functions before and after training, it was found that, among the three process parameters, the most influential factor for material removal was mesh, followed by force and speed. The results were consistent with the results of variance analysis in Section 4.1.