An ANFIS model for predicting workpiece deformation during the milling of thin-walled composite components

Abstract

The milling of thin-walled structural components is a meticulous procedure, particularly for workpieces with diminished mechanical qualities. Such workpieces are subject to greater deformation due to the cutting force. These deformations have a negative effect on the machining accuracy of the machined parts. Therefore, it is important to focus on the causes and effects of workpiece deformation in order to understand the surface machining quality of the workpiece. The aim of this study is to calculate the deflection of the workpiece during milling of thin-walled components made of glass fiber reinforced polymer composite (GFRP), carbon fiber reinforced polymer composite (CFRP) and basalt fiber reinforced polymer composite (BFRP). The tests were carried out at three different feed rates (0.1-0.2-0.3 mm/rev) and three different speeds (2000-3000-4000 r/min). A high-speed camera was used to detect the deflection of the thin-walled workpiece during milling. The effect of machining parameters on the variation of cutting force and deflection was statistically evaluated by analysis of variance. In order to better understand the behaviour of the parameters in the process, a predictive model was created using the artificial intelligence method Adaptive Network Based Fuzzy Inference System (ANFIS). The model is able to predict the cutting force with an accuracy of 65.7% and the deformation with an accuracy of 98.9%.