Professor of School of Engineering, Design and Built Environment, Western Sydney University, Australia. His research interests cover Industry 4.0, Additive Manufacturing, Advanced Engineering Materials and Structures (Metals and Composites), Multi-scale Modelling of Materials and Structures, Metal Forming and Metal Surface Treatment.
2024-02-24
2024-01-04
2023-11-02
Abstract—With advancement in technology, demand of materials having high tensile strength and high impact strength increases. Machining of these materials with the help of conventional methods, is rather difficult and non productive. To overcome this challenge, Non-conventional machining methods were discovered and wire electrical discharge machining (WEDM) is one of them. WEDM is one of the popular machining methods which is helpful in cutting complex geometries in hard to machine materials. Aluminium alloys are increasingly finding commercial usage in automotive and aerospace industries owing to light weight and high strength factors. Micro WEDM of Al 7000 series alloy which is widely used in commercial applications has been chosen as work piece material in the current study. Charge capacitance decides the energy input in a spark responsible for material erosion and machining accuracy in micro WEDM. Wire feed rate as a process parameter in micro WEDM has not been studied. Objective of present research is to study the effect of voltage, capacitance and wire feed rate on material removal rate and surface roughness while machining of Al 7000 series alloy. These two are important response parameters. Material removal rate tells us about the speed of machining process whereas surface roughness tells us about the finish of machined surface. Zinc coated brass wire of diameter 0.07mm is used on micro WEDM. Taguchi methodology has been chosen for design of experiment and L9 orthogonal array has been selected for present study. Replicates of experiment have been done to avoid any chances of random error. Analysis of variance and main effect plot have been used to find significant factors and their respective contribution on response variables. Multivariable optimization has been done using Grey Relational Analysis. Parametric levels are found at which both response parameters i.e. material removal rate and surface roughness have optimal values. The results obtained have been validated using conformational experiments.
