Artificial Neural Network Modeling of Abrasion Loss and Surface Roughness of Crab Carapace Impregnated Coir Vinyl Ester Composites

Abstract

Roughness plays an important role in determining how an object would be related with its environment. In tribology, rough surfaces easily obtain wear more quickly and have higher friction coefficients than smooth surfaces. Roughness is often a good analyzer of the performance of a mechanical component. This investigation is aimed to study the abrasion loss and surface roughness behaviors in crab carapace-filled coir fiber reinforced vinyl ester composites. The development of filler-impregnated fiber-polymer composites in recent years necessitated the evaluation and prediction of tribological behaviors in fiber reinforced composites. The composite fabrication was planned by varying the three fabrication parameters with three levels such as fiber length (10 mm, 30 mm, and 50 mm), fiber diameter (0.1 mm, 0.18 mm, and 0.25 mm), and content of crab carapace fillers (0%, 2%, and 4%) as per Design of Experiments (DOEs) in this current investigation. Low velocity integrated wear loss tests on composite samples were carried out, and also the average surface roughness is measured in the fabricated composites. Nonlinear regression equations were developed to study the correlation between tribological behaviors and fabrication parameters. The interaction effect of fabrication parameters was studied using ANOVA two-tail test and validated using response surface plots. In order to forecast abrasion loss and surface roughness behaviors, artificial neural network (ANN) models were constructed, and it was discovered that the produced ANN models effectively predicted the abrasion loss as well as surface roughness behavior within the given ranges.