Mechanical property prediction of SPS processed GNP/PLA polymer nanocomposite using artificial neural network. Issue 1 (1st January 2020)

• Record Type: Journal Article
• Authors: Adesina, O. T.; Jamiru, T.; Daniyan, I. A.; Sadiku, E. R.; Ogunbiyi, O. F.; Adesina, O. S.; Beneke, L. W.
• Editors: Pham, Duc
• Is Part Of: Cogent engineering. Volume 7:Issue 1(2020)
• Publication Date: 2020-01-01
• Subjects: ANN -- density -- hardness -- SPS -- PLA -- GNP; Engineering -- Periodicals; Technology -- Periodicals; Engineering; Technology; Periodicals; 620
• DOI: 10.1080/23311916.2020.1720894
• Languages: English
• ISSNs: 2331-1916

Abstract

Abstract: The quality and performance of composite-based materials are functions of their mechanical properties. Hence, a scientific basis is needed for the determination of the feasible combination of process parameters that will bring about excellent mechanical properties. This study examines the potential of artificial neural network (ANN) for the prediction of mechanical properties, namely density and hardness of graphene nanoplatelet (GNP)/polylactic acid (PLA) nanocomposite developed under various operating conditions of spark plasma sintering (SPS) technique. A back-propagation having a 2-12-2 architecture and Levenberg–Marquardt algorithm was developed to predict the mechanical performance in terms of density and hardness property of GNP/PLA nanocomposites. The predictions of the modelled results were compared with those of the experimental value obtained. The model gave a low root-mean-squared error and performed well with the correlation coefficient ( R ) for both outputs; density (0.95497) and hardness (0.9832) found to be close to 1. The results of the predicted data were discovered to be very consistent with the values obtained from the actual experimental test result. Thus, our study confirmed the efficiency of a well-trained ANN system in estimating the density and hardness property of SPSed GNP/PLA nanocomposites. Hence, the ANN technique is a reliable decision-making tool capable of reducing the excessive cost incurred in experimental characterisation for newly developed polymer composites. This will serve as a decision-making tool for manufacturing industries where SPS techniques will be employed for processing GNP/PLA polymer nanocomposite.