4.6 Article

Hybrid MCDM approach for examining the high-stress abrasive wear behaviour of in situ ZA-27/TiCp MMCs

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MATERIALS CHEMISTRY AND PHYSICS
卷 277, 期 -, 页码 -

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ELSEVIER SCIENCE SA
DOI: 10.1016/j.matchemphys.2021.125319

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ZA27 alloys; Microstructure; Wear behaviour; Surface roughness; GRA; ANOVA; RSM

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This study investigated the effects of TiCp incorporations and other process variables on the abrasive wear behavior of ZA27/TiC metal matrix composites. Through experiments and Grey Relationship Analysis, optimal processing conditions were determined, revealing the contribution of abrasive wear parameters to process properties and analysis using a second-order quadratic regression model.
This study examines the effects of TiCp incorporations, and other process variables such as abrasive grit sizes, applied loads, and sliding speeds on the high-stress abrasive wear behaviour of ZA27/TiC metal matrix composites. TiC particles were synthesized within the ZA27 matrix during the composite fabrication. The experiments were conducted based on a face-centered central composite design for the methodology of the response surface. Grey Relationship Analysis was carried out along with the desired functions to investigate the appropriate processing conditions for which the input variables have optimal performance characteristics. The nature and percentage contribution of abrasive wear parameters to the process properties have been identified by developing a second-order quadratic regression model. The observations showed that the wear rate, frictional heating, and friction coefficient decreased markedly for ZA27 base alloy. Improvements were noticed in the response characteristics attained by implementing both the GRSM and desirability approaches. Significant deviations (10.74% on wear rates, 6.5% on COF, and 1.5% on temperature) were obtained on responses implementing optimal parametric combinations predicted by the GRA-based hybrid approach. With the corresponding micrograph, the optical surface profile represents an improved quality of the surface and textures that use the GRSM hybrid approach to predict the process parameter settings.

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