Predictive Modeling and Analysis of Material Removal Characteristics for Robotic Belt Grinding of Complex Blade

Abstract

High-performance grinding has been converted from traditional manual grinding to robotic grinding over recent years. Accurate material removal is challenging for workpieces with complex profiles. Over recent years, digital processing of grinding has shown its great potential in the optimization of manufacturing processes and operational efficiency. Thus, quantification of the material removal process is an inevitable trend. This research establishes a three-dimensional model of the grinding workstation and designs the blade back arc grinding trajectory. A prediction model of the blade material removal depth (MRD) is established, based on the Adaptive Neuro-Fuzzy Inference System (ANFIS). Experiments were carried out using the Taguchi method to investigate how certain elements might affect the outcomes. An Analysis of Variance (ANOVA) was used to study the effect of abrasive belt grinding characteristics on blade material removal. The mean absolute percent error (MAPE) of the established ANFIS model, after training and testing, was 3.976%, demonstrating superior performance to the reported findings, which range from 4.373% to 7.960%. ANFIS exhibited superior outcomes, when compared to other prediction models, such as random forest (RF), artificial neural network (ANN), and support vector regression (SVR). This work can provide some sound guidance for high-precision prediction of material removal amounts from surface grinding of steam turbine blades.