Machine Learning Algorithms to Predict Wear Behavior of Modified ZA-27 Alloy Under Varying Operating Parameters

Algur, Veerabhadrappa; Hulipalled, Poornima; Lokesha, V.; Nagaral, Madeva; Auradi, V.

doi:10.1007/s40735-021-00610-8

Cited by 18 publications

(7 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To achieve the highest relative density, the AA6063 alloy was used. Increasing the percentage of ZrO 2 in the samples causes them to become less dense in comparison [24]. A high melting temperature of ceramic nanoparticles hinders compressibility and impairs consolidation, leading to a lower relative density and hence greater porosity [25].…”

Section: Experimental Densitymentioning

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

View full text Add to dashboard Cite

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.

show abstract

Section: Experimental Densitymentioning

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

View full text Add to dashboard Cite

show abstract

“…The first is the reinforcing phase, and the second is the matrix phase, which is typically an alloy of metals. The enhanced qualities of composites and the material properties that cannot be achieved by a single phase are both enhanced by combining the two phases [ 23,24]. In an effort to create MMCs with exceptional strength, researchers are devoting more time and energy to reinforcing development.…”

Section: Introductionmentioning

confidence: 99%

A review on constituents, applications and processing methods of metal matrix composites

S B Angadi,

Madeva Nagaral,

Nagaraj Namdev

et al. 2024

Int. J. Sci. Res. Arch.

View full text Add to dashboard Cite

Metal matrix composites (MMCs) exhibit superior mechanical and wear resistant properties, allowing them to be deployed in a variety of manufacturing applications, including structural, automotive, aerospace domains etc. Aluminium as the matrix material has numerous applications due to its high strength-to-weight ratio. The inclusion of hard ceramic particles in aluminium alloys increases overall strength and wear-resistance. It is important to know about different constituents of composite materials. In the present review paper an attempt has been made to highlight important constituents of metal matrix composites and their applications. Also, this review paper covers the various manufacturing techniques of metal composites.

show abstract

“…Graphite, whether in fiber or particle form, has long been appreciated for its unique combination of high strength and low density. Inexpensive tailor-made materials (MMCs) made of aluminum graphite particulate are produced using solidification techniques and are used in a wide range of engineering applications, including automotive components, bushes, and bearings …”

Section: Introductionmentioning

confidence: 99%

“…Inexpensive tailor-made materials (MMCs) made of aluminum graphite particulate are produced using solidification techniques and are used in a wide range of engineering applications, including automotive components, bushes, and bearings. 6 To fabricate aluminum composites with the desired properties, many researchers have taken advantage of reinforcement particles of varying shapes. To enhance the tribological properties of a matrix material, graphite is an ideal reinforcement.…”

Section: Introductionmentioning

confidence: 99%

Influence of Graphite Particles on the Mechanical and Wear Characterization of Al6082 Alloy Composites

Somayaji,

Nagaral,

Anjinappa

et al. 2023

ACS Omega

Self Cite

View full text Add to dashboard Cite

In the current study, a two-stage stir cast process was used to produce Al6082 reinforced with sized graphite particulates, and the material’s mechanical and tribological properties were analyzed. The graphite content in the Al6082 alloy was increased from 2 to 6% in steps of 2 wt %. The impact of graphite addition to Al6082 was evaluated using microstructural micrographs, hardness test, tensile test, and wear test outcomes. The matrix alloy’s microstructure and particle distribution were analyzed using scanning electron microscopy and energy-dispersive spectroscopy. The microstructure of Al6082 shows that the reinforcement particles are evenly distributed throughout the matrix. Although the hardness of metal–matrix composites was slightly reduced when graphite was added at concentrations of up to 6 wt %, the material’s tensile strength and wear resistance were significantly improved. Micrographs taken by a microscope were used to examine the fractured surfaces of tensile test specimens. Wear experiments were performed using a conventional pin-on-disc tribometer to examine the tribological properties of both unreinforced matrix and graphite composites. With the addition of 2, 4, and 6 wt % of graphite particles, the composites’ wear resistance was significantly improved. Wear of alloys and their composites was analyzed to determine how load and sliding speed impacted wear loss.

show abstract

Machine Learning Algorithms to Predict Wear Behavior of Modified ZA-27 Alloy Under Varying Operating Parameters

Cited by 18 publications

References 21 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 review on constituents, applications and processing methods of metal matrix composites

Influence of Graphite Particles on the Mechanical and Wear Characterization of Al6082 Alloy Composites

Contact Info

Product

Resources

About