Investigation of Surface Erosion Characteristics of AA7075–Nano-hybrid Surface Composite using COVI Gene Algorithm

• Published in: Journal of bio- and tribo-corrosion Vol. 11; no. 1
• Main Authors: Khan, Adam, Kumar, P. S. Samuel Ratna, Vignesh, Vaira, Mashinini, P. M.
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.03.2025; Springer Nature B.V
• Subjects: Algorithms; Alloys; Aluminosilicates; Aluminum base alloys; Aluminum oxide; Aluminum silicates; Biomaterials; Chemistry and Materials Science; Corrosion and Coatings; Electron back scatter; Emission analysis; Friction stir processing; Impact analysis; Machine learning; Materials Science; Scanning electron microscopy; Service life; Solid Mechanics; Surface layers; Surface properties; Surface roughness; Tribology; Wear rate; X-ray spectroscopy; COVI Gene algorithm; Erosion wear rate; EBSD analysis; Surface composite; AA7075
• ISSN: 2198-4220; 2198-4239
• DOI: 10.1007/s40735-025-00954-5

Aluminium alloys (AA) 7075 are utilized extensively in the aviation sectors for a variety of purposes. Throughout their service life, the structural components made of aluminium alloys AA7075 may be subjected to solid particle impingement. Further to enhance the surface properties of AA7075 material, MWCNT and aluminosilicate material were reinforced along the surface of the base material using Friction Stir Processing (FSP). The aluminium alloy AA7075 and surface composite microstructure were examined using electron back-scatter diffraction (EBSD) analysis. Aluminium oxide (Al 2 O 3 ) erodent particles were used in a comprehensive investigation on AA7075 and surface composite to assess erosive wear brought on by solid particle impacts. Impact angle of 90° was used to conduct the erosive wear studies based on ASTM G76-13 standard. An analysis was conducted on the morphology and surface roughness of the deteriorated materials utilizing a field-emission scanning electron microscope (FE-SEM), energy-dispersive X-ray spectroscopy (EDX), and laser profilometer. Thus, it was discovered that the hard erodent particle influences the surface texture, crater, and the rate of surface erosion were all impacted by erodent particles. To study the optimum FSP parameters that influence the minimum erosion wear rate machine learning (ML) technique, COVI Gene algorithm was proposed.