Composite Material Selection Analytics using Statistical and MCDM Techniques for Cotton-Glass Fibre
This article pertains to composite materials integrated with different materials like glass fiber/cotton/fillers characteristics for prediction of selection analytics. These composite materials are integrated by different layup process for various mechanical properties to find the perfect composite combination. There has to be a proper selection procedure technique to find out the proportion or probability of better composite material characterization. This article related to different types of multi-criteria decision making (MCDM) techniques and recommends use of these techniques for better decision-making process where the composite materials selections are limited and attributes of mechanical characteristics are in large numbers. Material selection prediction models with application prospects for final product characteristics are also discussed here for product designing and development. This paper also gives the glimpse on machine learning technique analysis for composite selection criteria as compared with MCDM techniques for future references and study. We have also studied some in-depth research work which has proved the material selection for its robust applications using MCDM Techniques. The study was tested with small demonstration that the composite dataset and material properties could be used in any MCDM approach, such as the COPRAS method. The co-relation and dependable matrix are built with high precision using the supplied independent variables.