Artificial neural network-based mechanical properties prediction of cellulose polyvinyl alcohol bio composite

Abstract

The deliberation of the environmental effect on synthetic polymer products has effectuated the development of the latest materials obtained from natural resources. This work tested the compatibility of a bio composite of polyvinyl alcohol (PVA) and cellulose. The mechanical effect of cellulose on PVA was investigated in the Synthia module of BIOVIA material studio and Artificial neural network. The exacted shear and bulk modulus were obtained with the variation of mass fraction of cellulose from 0 to 1 mass%. At 1% mass fraction of cellulose, the mechanical properties of PVA like shear modulus and bulk modulus had increased to 401% and 130%, respectively. The dry lab values of shear and bulk modulus were further analysed through a Bi-layer Artificial Neural Network (ANN). The designed ANN successfully predicted the bulk and shear modulus values. The implemented ANN prevailed with a root mean square error of 2.0692 and 1.865 for both properties.