Extraction of kaolin and tribo informative analysis of the Al-kaolin composite through machine learning approaches.

V S S Venkatesh, Guttikonda Manohar, Pandu Ranga Vundavilli, M M Mahapatra, Ashish Goyal, Abhijit Bhowmik

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Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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V S S VenkateshSchool of Mechanical Sciences, Indian Institute of Technology Bhubaneswar, Bhubaneswar, Odisha, 752050, India.

Guttikonda ManoharDepartment of Mechanical Engineering, C.V.R College of Engineering, Vastunagar, Hyderabad, Telangana, 501510, India.

Pandu Ranga VundavilliSchool of Mechanical Sciences, Indian Institute of Technology Bhubaneswar, Bhubaneswar, Odisha, 752050, India.

M M MahapatraSchool of Mechanical Sciences, Indian Institute of Technology Bhubaneswar, Bhubaneswar, Odisha, 752050, India.

Ashish GoyalDepartment of Mechanical Engineering, Manipal University Jaipur, Jaipur, Rajasthan, 303007, India. ashish.goyal@jaipur.manipal.edu.

Abhijit BhowmikDepartment of Mechanical Engineering, Dream Institute of Technology, Kolkata, 700104, India.

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Abstract

Microwave fabrication of aluminium composites has emerged as a novel and trending technique in the current industrial landscape due to its efficiency and energy-saving potential. In this study, Al-kaolin composites were fabricated using microwave energy techniques, focusing on predictive modelling of the microwave-assisted Al-kaolin composite's wear rate and coefficient of friction (COF). The fabricated composites were evaluated for hardness, wear rate, and coefficient of friction (COF) under varying parameters. It was observed that 4 wt% kaolin is the optimal reinforcement fraction, resulting in a 34% improvement in tensile strength, while hardness showed a consistent increase up to 4 wt% Kaolin, reaching a maximum value of 96 RHC. Additionally, wear rate and COF exhibited a decreasing trend with increasing kaolin content, indicating enhanced tribological performance. The lowest wear rate of 3.2 × 10⁻

Indexed as

Coefficient of frictionKaolinMachine learningMicrowave sinteringWear rate

Identifiers

PMID40246955

DOI10.1038/s41598-025-97782-x

PMCPMC12006395

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LicenceCC BY-NC-ND

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