Publication Type : Journal Article
Source : Emergent Materials, 2022
Url : https://www.webofscience.com/wos/woscc/full-record/WOS:000854417500001
Keywords : Friction stir processing, Surface composites, Machine learning, Microhardness, Surface temperature
Campus : Coimbatore
School : School of Engineering
Department : Mechanical Engineering
Year : 2022
Abstract : In this work, the friction stir process (FSP) is used to develop the nano/sub-micron-sized reinforced hybrid aluminum matrix surface composites to enhance the mechanical property of the material. To develop the hybrid surface composites, AA5083 matrix was reinforced with multiwalled carbon nanotube (MWCNT) in nano and aluminosilicate (Al2SiO5) sub-microns size material. The FSP experimental trials were performed by varying the process parameters using full factorial design. A machine learning tool (MATLAB R2020a software) analyzes the obtained result and optimizes the process parameter. COMSOL - Multiphysics simulation software was used to simulate the processed model to understand the temperature distribution throughout the plate and surface morphological change mechanism during the FSP by changing the process parameter. This work shows that the tool rotation speed of 1050 rpm and transverse speed 42 (mm/min) with a constant shoulder diameter of 18 mm show the optimum microhardness value of the developed hybrid surface composite plate.
Cite this Research Publication : Kumar, PSSR (Kumar, Samuel Ratna P. S.) ; Mashinini, PM (Mashinini, P. M.) ; Vignesh, RV (Vignesh, Vaira R.) "Experimental and numerical investigation of multiwalled carbon nanotube/aluminosilicate reinforced aluminum hybrid surface composites using friction stir processing", Emergent Materials, 2022