Optimization of Wear Properties in Aluminum Metal Matrix Composites using Hybrid Taguchi-GRA-PCA

This research created hot-pressed composites of the AA6063 matrix with varying concentrations of ZrO2 (0.25, 0.5, and 1 wt %). At sliding speeds of 80, 120, and 150 mm/s, the wear performance of the specimen was studied at loads of 10 N, 15 N, 20 N, and 25 N. The authors analyzed the counter-face material, the wear debris, and the worn surfaces to learn about the wear mechanisms. Developing these three machine learning (ML) algorithms was to evaluate the ability to predict wear behavior using the same small dataset collected using varying test processes. A thorough examination of each model hyperparameter tuning phase was performed. The predictive performance was analyzed using several statistical tools. The most effective decision-making algorithms for this data collection were those based on trees. Predictions made by the decision tree algorithm for the test and validation measurements have an accuracy of 86% and 99.7%, respectively. The best model was picked out based on the results of the predictions.

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“…Shearing the reinforcement particles requires more energy. Hence, the composite surface has a lower friction coefcient [42].…”

Section: Wear Test and Hardness Resultsmentioning

confidence: 99%

Investigation of the Wear Behavior of AA6063/Zirconium Oxide Nanocomposites Using Hybrid Machine Learning Algorithms

Roy

Shanmugam

Vinothini

et al. 2023

Journal of Chemistry

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“…It does not generate any wear debris, but surfaces are conditioned properly. This means that the lowest temperature is produced properly and dissipated in the interface eld, which occurs at the lower running time [30]. The action of deep grooves has been visible.…”

Section: Wear Surface Morphologymentioning

confidence: 93%

A combined approach to grey relational analysis and principal component analysis for development of input parameters in the wear characterization of Al7075 (SiC+Al2O3) hybrid metal matrix composites

Selvarasu,

Jayakumar

2023

Scientia Iranica

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The current research aims to study the e ect of mechanical stir casting on the Al7075-based lightweight alloy made of Al 2 O 3 and SiC at di erent times and speeds of the stirrer. As observed, the hybrid metal matrix improved the wear resistance of the composite material Al7075 (SiC+Al 2 O 3 ). Experimental designs were formulated using Taguchi's DoE and based on four levels and four parameters, L 16 OA was selected. Both Grey Relational Analysis (GRA) and Principle Component Analysis (PCA) were employed to calculate the matrix composition. In addition, Pin-On-Disc (POD) test was done to measure the wear resistance of the cast pins. Further, mechanical characterizations such as tensile, hardness, impact, density, and porosity were identi ed through testing. The optimum parameters in this study were SiC of 6%, Al 2 O 3 of 4%, stirrer time of 15 min, and stirrer speed of 600 rpm, which led to the low wear loss of 0.0034 g, superior micro hardness of 154.22 BHN, tensile strength of 443.61 MPa, and impact energy of 3 J. From the ANOVA results, the stirrer speed was the most in uential parameter among others with the share of 49.40%. The worn surfaces were identi ed through the Scanning Electron Microscope (SEM) and Energy Dispersive X-ray (EDX) analyses.

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“…Taguchi and GRA are best suited for finding optimum wear and friction force of the composite 6) . Multi-response optimization is a suitable method for optimization of different parameters 7) , 9) , 13) .…”

Section: Taguchi Designmentioning

confidence: 99%

Optimization of Stiffness Properties of Composite Sandwich using Hybrid Taguchi-GRA-PCA

Kumar¹,

Chanda²,

Angra³

2021

Evergreen

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The composite sandwich structures provide the benefits of higher Strength to weight ratio, higher stiffness and flexural strength. The different properties of a sandwich structure depend on various design factors such as material of core and facesheet, thickness of facesheet and core, type of core and panel shape etc. In this study, four different factors i.e. Material of Honeycomb core, Core Height, Facesheet Thickness and Panel Width, with each having three different values have been chosen and a L9 Orthogonal has been generated using Taguchi Design in Minitab 18. After that, 9 different samples have been fabricated as per configurations of L9 OA. Then three point bending test has been performed on these samples and the values of Bending Stiffness, Rigidity and Equivalent Stiffness has been calculated .Then Taguchi Analysis has been performed on the output responses and optimum design parameters have been detected. After that PCA assisted Grey Relation Analysis has been performed on output responses and the values of optimum design parameters for sandwich structure has been obtained.

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Optimization of Wear Properties in Aluminum Metal Matrix Composites using Hybrid Taguchi-GRA-PCA

Cited by 6 publications

References 27 publications

Investigation of the Wear Behavior of AA6063/Zirconium Oxide Nanocomposites Using Hybrid Machine Learning Algorithms

Investigation of the Wear Behavior of AA6063/Zirconium Oxide Nanocomposites Using Hybrid Machine Learning Algorithms

A combined approach to grey relational analysis and principal component analysis for development of input parameters in the wear characterization of Al7075 (SiC+Al2O3) hybrid metal matrix composites

Optimization of Stiffness Properties of Composite Sandwich using Hybrid Taguchi-GRA-PCA

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