Evaluating and Optimising Tribological Parameters of Enhanced Two-Step Stir Cast Al6061/Nano-SiO2 Composite Using Machine Learning Techniques

• Published in: Journal of bio- and tribo-corrosion 2024, Vol.10 (3), Article 66
• Main Authors: Votarikari, Naveen Kumar, Kishore Nath, N., Ramesh Babu, P.
• Format: Article
• Language: English
• Subjects: Aluminum base alloys; Artificial neural networks; Biomaterials; Chemistry and Materials Science; Composite materials; Corrosion and Coatings; Energy dispersive X ray analysis; Heat treatments; Machine learning; Materials Science; Mathematical models; Microhardness; Neural networks; Optimization; Orthogonal arrays; Parameters; Precipitation hardening; Precipitation heat treatment; Response surface methodology; Scanning electron microscopy; Silicon dioxide; Solid Mechanics; Surface properties; Tribology; Wear rate; Wear resistance; X ray analysis; X-ray diffraction
• ISSN: 2198-4220; 2198-4239
• DOI: 10.1007/s40735-024-00873-x

This study aims to analyse the potential of a two-step stir casting technique in producing Al6061/nano-SiO 2 composites to enhance wear resistance in dry sliding conditions. This approach then aims to enhance the dispersion of the nano-SiO 2 reinforcements to achieve enhanced anti-wear performance. Using the L27 orthogonal array, wear behaviour influenced by the different loads (5 N, 10 N and 15 N) has been investigated. For microstructural characterisation of the composite, scanning electron microscopy (SEM), energy-dispersive X-ray analysis and X-ray diffraction were conducted to observe the dispersion of reinforcements and any signs of interfacial activities. A mathematical model using response surface methodology was also incorporated to determine the composites’ optimum wear rate and COF for the three distances: 1000 m, 2000 m and 3000 m. The findings indicated that adding the nano-SiO 2 affected wear rate and COF, further supported by SEM images of the worn surfaces revealing that the 3 wt%. Thus, nano-SiO 2 composite shows enhanced wear resistance and hardness of the surface. To further examine the impact of wear parameters under various loads, an artificial neural network (ANN) was used. Furthermore, to improve the composites’ surface properties, they were also treated using heat treatment techniques, such as precipitation hardening, quenching and ageing. This treatment increased microhardness by 58% for the 3-wt% composite.