• HARINI M DEV
• VINOD KUMAR M S

The study of metal composites and its properties is an essential part of the field of study in the area of Composite materials. The composites of metals contributes towards the strength of metal structures being used in various Industrial applications such as Aviation, Construction and Production units etc. Various types of metal compositions will be considered in the different areas of applications. Among them, the Aluminum composites are very special and mostly used in lot of metallic design solutions. The composites of Aluminum, Magnesium and Tri-Boride is one such composition which has been the study of material in this work. Amongst various traditional studies, the classification of percentage of compositions is one of the important field of study. In this work, a novel AI driven computational approach comprising of Deep learning technique has been presented to automatically detect and classify the concentration of metal composites of the above three material on the microscopic images obtained from Scanning Electron Microscope(SEM). This approach outperforms other traditional approaches in a way that does not require any standard mechanical process and seamlessly will be able to figure out the concentrations for any unknown samples.

Composite Materials, Deep Learning, Aluminum, Magnesium, Tri-Boride, ResNet34

"Classification of Aluminum, Magnesium and Tri-Boride Metal composites using Deep Learning Approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 7, page no.b427-b432, July-2021, Available :http://www.jetir.org/papers/JETIR2107181.pdf

"Classification of Aluminum, Magnesium and Tri-Boride Metal composites using Deep Learning Approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 7, page no. ppb427-b432, July-2021, Available at : http://www.jetir.org/papers/JETIR2107181.pdf