Comparative analysis of thrust force, roughness and roundness error in drilling of aluminium composites using RSM, ANN and fuzzy logic

Abstract

Making holes without any defect in a solid product made of hybrid Aluminium composite material, is a difficult job in assembly industries. Hybrid composite of Aluminium 7075 reinforced with ceramic materials like silicon carbide, boron carbide, graphite and mica are used in automobile and structural industries due to their excellent mechanical properties. Thrust force developed during drilling, Error in the circularity and poor surface finish of the drilled holes are some of the common problems faced in the drilling process. Hence, to optimise the quality of drilling, analysing these responses under various conditions of drilling by varying the drilling factor become essential. This study discusses the development of various models to predict the thrust force developed, roughness and circularity error in the holes drilled on a hybrid metal matrix composite. Testing of drilling is conducted in CNC vertical machining centre using Titanium aluminium nitride (TiAlN) coated carbide drill tool of 5 mm diameter. Various drilling factors considered in our study are point angle of the drill tool, drilling speed and feed rate. Multiple regression equations using RSM, Artificial neural network (ANN) and Fuzzy Logic algorithms are used to develop the prediction models. The predicted values of thrust force, surface roughness and circularity error from these models are found to be matching with the observed experimental values.