Evaluation of mechanical property of banana HDPE composite through experiment and artificial neural network (ANN)

Abstract

In this study, two post-curing techniques using microwave and oven were utilized to examine the mechanical characteristics and behavior of composites reinforced with banana fibers with its matrix made of high-density polyethylene polymer. The research involved experimental work through tensile tests and predictive modelling using the artificial neural network (ANN). The first part of the research involved the collection of datasets which were gathered from the tensile tests based on the ASTM D638 using dog bone specimens of various fiber percentages to compare the mechanical properties such as the specific energy and maximum load after post-curing using an oven and microwave at various powers and time. The trend and behavior of the composite due to the fiber percentages and the effects of post curing methods were analyzed. The second part of the research involved the ANN approach to predict the maximum gage length based on the data collected from the experiments using inputs such as the fiber content, maximum load, and displacement. Further optimization was done on the ANN model by fine-tuning variables such as the learning function, transfer function, and number of neurons to predict the stress and strain values. The accuracy of the results was calculated using statistical evaluation equations. The optimized settings were utilized to generate the predicted results and the deviation from the experimental results were evaluated.