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 rate (MRR) is established based on the Adaptive Neuro-Fuzzy Inference System (ANFIS). Experiments are carried out using the Taguchi method to investigate how certain elements might affect the outcomes. An Analysis of Variance (ANOVA) is 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 is 3.976%, demonstrating superior performance to the reported findings, which range from 4.373 to 7.96%. ANFIS exhibits 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.

Prediction of contact characteristics of abrasive belt compliant grinding for aircraft blades

“…Under the guidance of a hyperelastic rubber contact wheel, abrasive belt contacts the blade surface and makes relative movement to remove material from workpiece. It is an important means for the precision grinding process of weakly rigid curved blades [3][4][5].…”

Point Cloud Based Model-free Path Planning Method for grinding Large Complex Forging Parts

“…The modified model of grinding stress distribution was developed to calculate the depth of grinding based on the elastic mechanics model of the power series method, accurately predicting the cutting depth of the robotic belt grinding system[13]. Jia et al established a 3D model of a grinding workstation and designed the arc grinding trajectory of blade rear, providing theoretical guidance for the high-precision prediction of material removal during turbine blade surface grinding[14]. Zhu et al introduced the concept of six-point positioning into the dynamic workpiece coordinate frame calibration process using a point laser displacement sensor (PLDS), achieving higher precision calibration results and improving operational efficiency[15].…”