ItemArticle

Experimental, FEA, and machine learning studies on wear behavior of LM13 aluminum hybrid composites reinforced with zircon and graphite

Title
Experimental, FEA, and machine learning studies on wear behavior of LM13 aluminum hybrid composites reinforced with zircon and graphite
Creator
Yellampalli Prakash, Ravitej; Ramakumar, Bommishetty Venkata Naga; R, Keshavamurthy; Patil, Sandeep S; Vijay Kumar, Vishnu; Hiriyannaiah, Adarsha
Description
This paper examines applied load and zircon reinforcement influence on LM13 alloy composites wear behavior. LM13 was reinforced with 3?wt.% graphite with 3, 6, 9, and 12 weight percent of zircon utilizing a stir casting technique with a chill end to achieve unidirectional solidification. Wear tests were conducted on specimen's chill end using a pin-on-disc apparatus under loads of 30?N, to 70?N in steps of 10?N incremental. The results indicated that when the amount of zircon went up, the wear rate dropped, reaching a minimum at 9?wt.% zircon, then slightly increasing at 12?wt.%. Specifically, wear rate reduced from 4.2?10?3mm/Nm at 3?wt.% zircon to 2.7?10?3mm/Nm at 9?wt.% zircon, before rising to 3.5?10?3mm/Nm at 12?wt.%, establishing 9?wt.% zircon as the optimum reinforcement. Finite Element Analysis (FEA) had been used to simulate wear behavior, and its predictions aligned well with experimental data, with deviations under 5%. Both experimental and FEA results confirmed that wear rate increases proportionally with applied load. Additionally, machine learning techniques were employed to validate the observed trends, enhancing the reliability of the findings. Microstructural analysis through Field Emission Scanning Electron Microscopy showed evidence of plastic deformation and delamination at higher stress levels, compromising material integrity. Notably, the composite with 9?wt.% zircon exhibited reduced wear deformation and minimal microstructural damage, confirming its effectiveness in improving wear resistance. IMechE 2025
Source
Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology;Issue;;Article No.;1.35065012513675e+16;
Date
01-01-2025
Publisher
SAGE Publications Ltd
Subject
finite element analysis; Machine learning; Pin On Disc; stir casting; wear rate
Coverage
Yellampalli Prakash R., Department of Mechanical Engineering, Dayananda Sagar University, Karnataka, Bengaluru, India; Ramakumar B.V.N., Department of Aerospace Engineering, Dayananda Sagar University, Karnataka, Bengaluru, India; R K., Department of Mechanical and Automobile Engineering, CHRIST University, Karnataka, Bengaluru, India; Patil S.S., RWTH India Office, Maharashtra, Pune, India; Vijay Kumar V., Division of Engineering, NYU, University of Abu Dhabi, Abu Dhabi, United Arab Emirates; Hiriyannaiah A., Department of Mechanical Engineering, Jain University, Karnataka, Bengaluru, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 13506501; CODEN: PEJTE
Format
online
Language
English
Type
Article

Citation

Yellampalli Prakash, Ravitej; Ramakumar, Bommishetty Venkata Naga; R, Keshavamurthy; Patil, Sandeep S; Vijay Kumar, Vishnu; Hiriyannaiah, Adarsha, “Experimental, FEA, and machine learning studies on wear behavior of LM13 aluminum hybrid composites reinforced with zircon and graphite,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/23167.

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