Optimizing the selection of natural fibre reinforcement and polymer matrix for plastic composite using LS-SVM technique.

The manufacturing sector is paying close attention to plastic matrix composites (PMCs) reinforced with natural fibres for improving their products. Due to the fact that PMC reinforced with naturally occurring fibres is more affordable and has superior mechanical qualities. Based on the application material requirements, An important step in the production of PMC is choosing the right natural fibres for reinforcing and determining how much of each. This investigation aimed that Artificial Intelligence (AI) or soft computing based approaches are used to determine the right amount of natural fibres in PMCs to make the manufacturing process simpler. However, techniques in the literature are not concentrated on finding suitable material. Hence in this investigation, a local search with support vector machine (LS-SVM) optimization technique is proposed for the optimal selection of appropriate proportions of suitable fibres. Modelling of the Proposed LS-SVM Optimization was demonstrated. In this proposed technique around four kinds of polymers/plastics and 14 natural fibres are considered, which are optimized in various proportions. The optimization performance is evaluated based on the tensile strength, flexural yield strength and flexural yield modulus. The proposed LS-SVM Optimization was evacuated by developing solutions for medical applications (Case 1), Transportation applications (Case 2) and other notable applications (Case 3) in terms of tensile and flexural properties of the material. The maximum flexure stress in case 1, case 2, and case 3 is observed as 53 MPa, 45 MPa and 26 MPa respectively. Similarly, the maximum flexure stress in case 1, case 2, and case 3 is observed as 53 MPa, 45 MPa and 26 MPa respectively. Hence the proposed method recommended for choosing optimal decision on the choice of fiber and their quantity in the composite matrix.