Predicting wear behavior of AZ31/TiC composites produced via ultrasonic vibration assisted friction stir processing using machine learning models.
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| Title: | Predicting wear behavior of AZ31/TiC composites produced via ultrasonic vibration assisted friction stir processing using machine learning models. |
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| Authors: | Kumar TS; Department of Mechanical Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, 641112, India. t_satishkumar@cb.amrita.edu., Shalini S; Department of Physics, PSG Polytechnic College, Coimbatore, India., Petrů J; Department of Machining, Assembly and Engineering Metrology, Faculty of Mechanical Engineering, VSB-Technical University of Ostrava, 70800, Ostrava, Czech Republic., Mishra MK; Department of Metallurgical and Materials Engineering, Malaviya National Institute of Technology Jaipur, Jaipur, 302017, India., Kalita K; Department of Machining, Assembly and Engineering Metrology, Faculty of Mechanical Engineering, VSB-Technical University of Ostrava, 70800, Ostrava, Czech Republic. kanakkalita02@gmail.com.; Department of Mechanical Engineering, Rajalakshmi Institute of Technology, Chennai, 600124, India. kanakkalita02@gmail.com. |
| Source: | Scientific reports [Sci Rep] 2026 Mar 24; Vol. 16 (1). Date of Electronic Publication: 2026 Mar 24. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE; PubMed not MEDLINE |
| Database: | MEDLINE Ultimate |
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