An Experimental Investigation of Machining Parameters on Aluminum Composites
Authors
KamalKishor Maniyar, SunilKumar Harsur, Sagar N. Shinde, Jahier Abbas Shaaikh, Chandan M.N., Shobha Rupanar
Journal: Journal of Polymer & Composites, Volume 12, Special Issue 6, Pages S8–S13
Published Date: October 08, 2024
Abstract
The need for a material with good mechanical, thermal, and wear-resistant properties is satisfied by aluminum composite. However, the biggest obstacle to substituting it with alternative materials is the machining challenges. For this kind of hard-to-cut material, electric discharge machining (EDM) is a very efficient method. This study uses Taguchi’s method to determine the most advantageous input parameters for EDM of aluminum composites, focusing on improving material removal rate (MRR).
Introduction
Metal-matrix composites (MMCs) and hybrid MMCs offer improved mechanical properties for industrial use. Traditional machining methods face challenges with these materials, making EDM an efficient non-conventional process. This study focuses on optimizing EDM parameters for machining aluminum composites reinforced with SiC and graphite particles using the Taguchi method and analyzing the material removal rate.
Experimental Performance
The aluminum composites were fabricated using stir casting, incorporating 5%, 10%, and 15% SiC-Gr reinforcements. An L27 orthogonal array was used for experimental trials. Input parameters included current, pulse-on time, and reinforcement percentage. The electric discharge machining was performed using the ELECTRONICA-ELECTRAPULS PS 50ZNC machine.
Results and Discussion
The study found that the current was the most significant parameter influencing MRR, followed by pulse-on time and reinforcement percentage. The optimal EDM parameters for achieving a higher MRR were identified as 12 Amp current, 500 µs pulse-on time, and 15% reinforcement.
Conclusion
The Taguchi method successfully optimized EDM parameters for aluminum composites. Current had the highest impact on material removal rate, followed by pulse-on time and reinforcement percentage. Future studies can explore advanced optimization techniques like genetic algorithms and machine learning to enhance accuracy and efficiency.