Prediction of the Production Quality During Flat Bottom Drilling of Low Lead Brass Alloy Using Fuzzy Logic and Regression Models

Abstract

The approved restrictive rules on the containment of Lead element in the chemical composition of the brass alloys which are utilized in drinking water and pumping systems have resulted in developing of new material generations. Neglection or limitation of this element has faced the industry with some serious problems such as lower machinability as compared to the conventional ones. Furthermore, since the most application of the manufactured components from these alloys corresponds to the fluids transfer, the permeability property between the parts and their surface becomes prominent. In other words, any burrs or extra material which are left on the surfaces of manufactured components will make assembly troublesome, causing seals to tear, and permeability problems in the user's hands. In this study, the quality of the machined blind holes with flat bottom drills with various geometries including radial, axial rake angle as well as the cutting edge-radius have been investigated for machining of low-lead brass alloy. Moreover, it has attempted to develop fuzzy logic and regression models in order to predict the machined holes burr height and surface quality. The model predictions have been compared with the experimental data. The obtained results have demonstrated that the developed models are able in predicting of the product quality.