Journal
EMERGENT MATERIALS
Volume 5, Issue 6, Pages 1973-1983Publisher
SPRINGERNATURE
DOI: 10.1007/s42247-022-00408-5
Keywords
Friction stir processing; Surface composites; Machine learning; Microhardness; Surface temperature
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In this study, the friction stir process was used to develop nano/sub-micron-sized reinforced hybrid aluminum matrix surface composites. The optimum process parameters were determined through experimental trials and simulation.
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.
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