Abstract
The machining of aluminium metal matrix composites in CNC high speed conditions is significant because such composites have diverse applications in the aeronautics industry. Because that industry requires high quality outcomes, the prediction of surface roughness, which depends on input process parameters, assumes significance in the maintaining quality of products. Even though many researchers have worked in the area of conventional machining, very few of them have explored optimization techniques, such as teaching-learning-based optimization (TLBO) and gravitational search algorithms (GSA) in high speed environments. In this research, an attempt is made to determine the optimum machining conditions for the end-milling of composite materials using GSA. Input process parameters, such as cutting speed, feed, the depth of cut and the step-over ratio are taken as independent variables, and surface roughness is taken as dependent variable. Experiments were conducted on Al 2O3 + SiC metal matrix composite by considering selected variations in the input process parameters. Surface roughness is measured in each of cases, and the required data is obtained for further analysis. An empirical relationship is established between dependent and independent variables in the form of linear and non-linear regression equations, and the results are analysed. The results showed that GSA gives better results for surface roughness when compared to the genetic algorithm, simulated annealing and TLBO methods. An additional set of experiments was conducted to validate the results obtained.
Highlights
High speed milling (HSM) has assumed importance in recent years due to increased demand for quality, productivity and cost reduction in manufacturing [1]
The microstructure of the composite material of the selected combination showed no significant difference before and after machining, indicating the material is stable over the selected range of speed variation
Four heuristic methods (GA, simulated annealing (SA), teaching-learning-based optimization (TLBO) and gravitational search algorithm (GSA)) are used for optimization. All these four techniques are fundamentally different from each other: genetic algorithm (GA) is based upon the survival of fittest, SA is based upon annealing, TLBO is inspired by the teaching-learning method and GSA
Summary
High speed milling (HSM) has assumed importance in recent years due to increased demand for quality, productivity and cost reduction in manufacturing [1]. The machining of the aluminium metal matrix composite is a significant high-speed milling application. This technology has wide application in the aeronautics or aerospace sectors, and the moulds and die industry [2] and [3]. A metal matrix composite is made by combining at least two constituent parts, one of which must be a metal. The objective involved designing a metal matrix composite material mainly by adding the desirable attributes of metals and ceramics. The steps involved with castings of metal matrix composite are as follows: Melting the aluminium metal with 5, 10, 15 and
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