Machinability, modelling and statistical analysis of in-situ al–si–tib2 composites

Abstract

Aluminum and its alloys have numerous applications in manufacturing, aerospace, and automotive industries. At elevated temperatures, they start to fail in fulfilling their roles and functions. Aluminum-based metal matrix composites (MMCs) are good alternatives for metal and alloys due to their excellent properties. However, the conventional machining of several composites shows complications for a number of reasons, such as high tool wear, poor surface roughness, high machining cost, cutting forces, etc. Numerous studies have already been conducted on the machinability of various MMCs, but the machinability of Al–Si–TiB2 composite is still not well studied. It is of utmost importance that several process parameters of conventional machining are precisely controlled as well as optimized. In this study an effort was made to optimize input parameters such as cutting speed, depth of cut, and feed to obtain well-finished final components with the minimum cutting force and tool wear. These progressions are involved with multiple response characteristics, therefore the exploration of an appropriate multi-objective optimization technique was indeed essential. The performance characteristics of cutting forces and surface roughness were considered for optimization of the machining parameters. Analysis of variance (ANOVA) was employed for the optimization and statistical analysis.