Wear Behaviour of Al-SiCp Metal Matrix Composites and Optimization Using Taguchi Method and Grey Relational Analysis

Abstract

Aluminium metal matrix composite is a relatively new material that has proved its position in automobile, aerospace and other engineering design applications due to its wear resistance and substantial hardness. Need for improved tribological performance has led to the design and selection of newer variants of the composite. The present investigation deals with the study of wear behaviour of Al-SiCp metal matrix composite for varying reinforcement content, applied load, sliding speed and time. Aluminium metal matrix composites reinforced with SiC particles are prepared by liquid metallurgy route using LM6 aluminium alloy and silicon carbide particles (size ~ 37 µm) by varying the weight fraction of SiC in the range of 5% -10%. The material is synthesized by stir casting process in an electric melting furnace. The materials are then subjected to wear testing in a multitribotester using block on roller configuration. A plan of experiments based on L27 Taguchi orthogonal array is used to acquire the wear data in a controlled way. An analysis of variance is employed to investigate the influence of four controlling parameters, viz., SiC content, normal load, sliding speed and sliding time on dry sliding wear of the composites. It is observed that SiC content, sliding speed and normal load significantly affect the dry sliding wear. The optimal combination of the four controlling parameters is also obtained for minimum wear. The microstructure study of worn surfaces indicates nature of wear to be mostly abrasive.

Optimisation of process parameters for minimum volumetric wear rate on AA7075-TiC metal matrix composite

“…it can be observed that the sliding velocity and sliding distance have a significant effect on the volumetric wear rate. This is in agreement with the laws of adhesive wear and Archard's equation [30]. It is known that as the tensile strengths of the alloys increase, their volumetric wear rate decreases [31].…”

Modeling and Analysis of Mechanical Properties of Aluminium Alloy (A413) Processed through Squeeze Casting Route Using Artificial Neural Network Model and Statistical Technique

“…Several authors have optimized the squeeze casting process parameters for various Aluminum alloy grades using Taguchi method, Grey relational analysis, and genetic algorithm. It was reported that squeeze pressure, die preheating temperature, and melt temperature were found to be the major contributing factors on the mechanical properties of the castings and evaluated the optimal solutions for each factor [22][23][24][25][26][27][28][29]. Among all the available methods of Design of Experiments (DOE), Full Factorial Design (FFD) is a strong candidate in evaluating significant process parameters that can be employed to develop the casting process by evaluation of combined independent factors [30].…”

Multi-response optimization of dry sliding wear parameters of AA6026 using hybrid gray relational analysis coupled with response surface method

“…Investigation of the dry sliding wear behavior of aluminum alloy reinforced with SiC was done by Algur et al 7 and to receive data in a controlled way; the design of experiment (DoE) approach was enrolled using Taguchi’s method. Taking into consideration of process parameters such as pertinent to two-body abrasive wear behavior and multiple quality attributes of the material, the optimization of hybrid aluminum composites was done by Ghosh et al 8 using Taguchi’s gray relational analysis (GRA).…”