Mathematical modelling for prediction of mechanical properties of abutilon indicum fibre reinforced composite using RSM and ANN. (6th June 2022)

• Record Type: Journal Article
• Authors: Krishnudu, D. Mohana; Sreeramulu, D.; Reddy, P. Venkateshwar
• Is Part Of: International journal of engineering systems modelling and simulation. Volume 13:Number 2(2022)
• Page Start: 111
• Page End: 116
• Publication Date: 2022-06-06
• Subjects: abutilon indicum -- mechanical properties -- response surface methodology -- RSM -- artificial neural network -- ANN; Engineering systems -- Computer simulation -- Periodicals; Engineering systems -- Mathematical models -- Periodicals; 620.0042
• Languages: English
• ISSNs: 1755-9758

Abstract

Major application areas of natural fibre composites are found in packing industries. The focus of the present study is on natural fibre (abutilon indicum) reinforcement composite materials with filler and to predict the mechanical properties of abutilon indicum fibre reinforced composites. In the present study, fibre content (wt.%) and filler content (wt.%) are taken as the influencing parameters and mechanical properties like tensile, impact and flexural strengths are taken as an output parameter. Various proportions of fibre content and filler content were designed as per L25 orthogonal array and their mechanical properties were found. A regression equation was developed using RSM for each mechanical property in terms of fibre weight and filler weight. Mathematical models are developed by using both RSM and ANN and predictive capability of these two techniques were compared based on its R 2 values. From the R 2 values it is confirmed that the predictive capability of ANN model is higher than the RSM model for the present study.