Optimization of Machining Parameters and Performance Analysis of AA2024/ZrO2 Metal Matrix Composite Using TOPSIS: Insights into Squeeze Casting and Tribological Behavior

Abstract

For the past two decades, composites have played an essential role in Engineering. A perceptive amalgamation of two or more substances yields a synergistic effect unattainable by alternative methods. Composites are materials that physically combine two or more phases to create something better than the sum of its parts. The proper selection of processing parameters is the primary determinant of the final component cast configuration. To get the best castings, the casting parameter has to be fine-tuned. In many cases, the final product features are affected by more processing parameters. Squeeze casting produces metal matrix composites; however, optimizing process parameters is necessary to achieve a superior product. Despite the significance of the applied pressure, the die and melt temperatures are the most critical process parameters affecting the outcome. Squeeze casting is effective for steel and copper alloys, cast iron, and other metals. The melting temperatures of AA2024 were investigated at 710, 740, 770, and 800 degreesC, and their impacts were analyzed. To investigate potential improvements in mechanical characteristics, it is vital to know whether the microstructure and particle distribution are stable after squeeze casting and whether process parameters influence the reduction of porosity. Furthermore, it investigates the response of the AA2024/nZrO2 nanoparticles metal matrix composite to variations in melt and die temperatures, together with squeezing pressure. The two primary components of wear rate (WR) analysis for AA2024/nZrO2 composites are assessing wear resistance and material loss during sliding. Sliding speed, load, and surface configuration are considered while calculating wear performance. Metal matrix composites made of AA2024 and nZrO2 can have material removed using electrical discharge machining (EDM). A Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) is employed to find the optimum machining factors through the weight of entropy weight method (EWM). EDM is great at cutting complicated shapes and rigid materials. Still, it's essential to control the process parameters so the nZrO2 nanoparticles are prevented from being damaged and the dimensions remain consistent.

Topics & Concepts