## 1 Introduction
In addition, the main cause of the significant discrepancy between the model calculation result and the actual one is, frequently, due to disregarding the actual grinding scenario. A large amount of experimental data is obtained from the actual grinding parameters, and this is a more appropriate method in using the nonlinear regression model over the traditional model. Over the last few years, the application of machine learning algorithms has attracted more and more attention in the fields of manufacturing and processing [14; 15]. Furthermore, future development will increasingly favor the use of big data to advance the manufacturing and processing industries. [PERSON] et al. [16] proposed a polishing algorithm utilizing the composition of neural networks (NNWs) and genetic algorithms (GAs). [PERSON]'s algorithm solves the problem of uneven distribution of materials removed from the surface to be polished. More specifically, the effectiveness of the algorithm is verified by polishing experiments on uneven surfaces. [PERSON] [17] proposed a machine learning and acoustic sensing approach for Inconel 718 robot belt abrasive material removal. The material removal model included a newly trained and improved K-fold-XGBoost algorithm. Data-driven models have become a hot topic in the engineering world with the emergence of machine learning and deep learning algorithms. This argument is satisfactorily made by [PERSON] [18]. This paper's method, ANFIS, is in line with how data-driven models are applied in the engineering discipline. While other literature studies use rigid tools or flexible tools to grind flat workpieces, this study makes a unique addition by using flexible grinding tools to grind curved workpieces [19]. Support vector machine (SVM), neural network (NN), and fuzzy logic (FL) have been the three most popular learning techniques over the past 20 years. SVM, NN, and FL were the three comparison algorithms used by [PERSON] [20]. [PERSON] [21] used offline machine learning to deburr vibration data using wavelet decomposition, the Welch technique, and ANFIS. They also contrasted the ANFIS classifier against the SVM classifier, neural network classifier, and both. [PERSON] [14] carried out thorough comparisons with grinding data using methods like ANFIS, Artificial Neural Networks (ANN), Support Vector Regression (SVR), Random Forest (RF), etc. Based on the research in the above literature, this paper used the data obtained from the experiments to compare and analyze the four regression algorithms (ANFIS, ANN, SVR, and RF), and this provided a scientific basis for the selection of the ANFIS method. In this study, the three grinding factors, speed, mesh, and force, which were suggested to have the biggest effects on the material removal depth (MRD) in previous literature, are modeled. Firstly, orthogonal experiments were designed, using the Taguchi method, to reduce the number of experiments performed in order to find the optimal solution. Secondly, three-dimensional modeling of the robot hand-held blade grinding was applied to the actual grinding process. Finally, the regression model ANFIS was used to model the experimental results, and the influence of different parameters on material removal was studied. The reason for choosing ANFIS model will be discussed later. In contrast with the traditional linear modeling method, the relationship between the grinding tool and the workpiece was non-linear. The ANFIS method exploited in this paper is more suitable for practical situations. Therefore, it can predict the material removal depth without experiments. The remainder of the paper is organized as follows: Section 2 introduces the theoretical basis of belt grinding and the basic structure of ANFIS. Section 3 conducts trajectory planning and orthogonal experimental design for the robot abrasive belt grinding steam turbine blade experiment. Moreover, the experimental results in Section 3 are analyzed and discussed in Section 4. Finally, the discussion results are summarized and conclusions drawn.