Optimization of nonlinear characteristics of ball burnishing process using Sugeno fuzzy neural system

Abstract

Aluminium alloy (Al 63400) is burnished using different burnishing parameters. It deals with the modelling of nonlinear characteristics of ball burnishing using Sugeno fuzzy neural system. A fuzzy neural network or neuro-fuzzy system is a learning machine that finds the parameters of the fuzzy systems (i.e. fuzzy sets, fuzzy rules) by exploiting approximation techniques from neural networks. Input parameters are designed for experimental process using Box–Behnken method. Ball burnishing tool is used in CNC machining centre to surface finish the milling process. The tool and work-piece material are tungsten carbide and aluminium, respectively. The input parameters are force, table feed, step over, ball diameter and number of passes. The output parameters are surface roughness along feed direction, surface roughness across feed direction and surface micro-hardness. The minimum surface roughness obtained for ball burnishing process along the tool path (x-axis) is 0.032 μm and across the tool path (y-axis) is 0.232 μm and its micro-hardness is 91.63 HV. Sugeno fuzzy neuro system is used to model the nonlinear characteristics of the surface roughness (along the tool path and across the tool path) and micro-hardness. The Pearson product moment correlation is used to validate the fuzzy neuro model.