Process Parameters Optimisation of WEDM for AI SiC Metal Matrix Composite

Abstract

Background: Due to the very complex nature of the WEDM process, selection of favourable parameters by traditional methods is not satisfactory with specific regard to material removal rate and surface finish. Because of this, the researchers are inclined to employ mathematical techniques to optimise the process parameters for the best yield of cutting performance in WEDM. To employ the mathematical techniques, it is essential to understand and model the process mathematically that can establish the relationship between input and output. newlineAim: The objective of the present work is to develop the simulation model to predict the process parameters and validated by an experimental method using a wide range of inputs. It also involves the optimisation of the required process parameters for the desired design. newlineMaterials and Methods: The objective of the present work will be executed using Aluminium based metal matrix composites materials for experimentation work and Taguchi method for experimental investigations and Grey relational analysis for optimisation of process variables. newlineResults and discussion: Process parameters for the desired Material Removal Rate, surface Roughness and kerf will identify which will enable the industry to machine the composite materials efficiently. The present simulation model will also be extended to determine the process parameters for many other advanced engineering materials. The present work will also enable the industry to design any complex contour components for the required application without handicapped by the machining process. newlineConclusions: The most significant process parameter for the WEDM process is found to be a pulse on time in conjugation with the wire tension, and the less significant parameter is pulse off time. This suggests that the higher pulse-on time leads to higher material removal rate due to higher heat flow from high current during the WEDM process as mostly reviewed from literature. Most of the researchers have opted for single objective optimisation taking