ANFIS-based prediction and optimization of tribological performance in graphene-boron carbide reinforced C355 aluminium alloy hybrid nanocomposites.

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Title: ANFIS-based prediction and optimization of tribological performance in graphene-boron carbide reinforced C355 aluminium alloy hybrid nanocomposites.
Authors: Vellingiri S; Department of Mechanical Engineering, KIT-Kalaignarkarunanidhi Institute of Technology (Autonomous), Coimbatore, 641402, Tamil Nadu, India. winsureshv2011@gmail.com., Naresh J; Department of CSE (Artificial Intelligence and Machine Learning), Kommuri Pratap Reddy Institute of Technology, Ghanpur, Telangana, 500088, India., Kullegowda AG; Centre for Materials Testing Lab, Department of Mechanical Engineering, AMC Engineering College, Bengaluru, 560083, India., Settu P; Department of Mathematics, KIT-Kalaignarkarunanidhi Institute of Technology (Autonomous), Coimbatore, Tamil Nadu, 641 402, India., Yuvaraj KP; Department of Mechanical Engineering, Sri Krishna College of Engineering and Technology, Coimbatore, 641008, India., Bezawada S; Department of Mechanical Engineering, Siddharth Institute of Engineering & Technology (SIETK), Tirupati, Andhra Pradesh, 517586, India., Murugan A; Department of Mechanical Engineering, Debre Markos University, 269, Debre Markos, Ethiopia., Dukkipati S; Department of Electrical and Electronics Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, 522302, India.
Source: Scientific reports [Sci Rep] 2026 Jul 13. Date of Electronic Publication: 2026 Jul 13.
Publication Type: Journal Article
Journal Info: Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE
Database: MEDLINE Ultimate
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ISSN:2045-2322
DOI:10.1038/s41598-026-59910-z