Durability analysis on properties of water soaked PNNCs and CS-ANN model for wear property analysis of PNNCs

Abstract

The work aims to prepare and characterize polyester nanoclay nanocomposite (PNNCs) with various nanoclay weight percentages (0, 2, and 4). Nanoclay and polyester resin are blended using a mechanical stirrer followed by a sonicator. The blend is molded as specimens as per ASTM standards. The addition of nanoclay improved tensile strength by 12% to 16% and flexural strength by 4% to 10%. After 60 days of soaking in, the tensile strength retention rate of pure PE, 2PNNC, and 4PNNC are 84.9%, 89.8%, and 90.6%, respectively. At the same time, flexural strength retention rates of pure PE, 2PNNC, and 4PNNC are 86.7%, 90.2%, and 91.9%, respectively. SEM images are analyzed to know the reasons for specimen failure under tensile load. ExpDec1 (“One-phase exponential decay function with time constant parameter”) model is used on the experimental data to determine the composite’s durability. The experimental values and data produced by the ExpDec1 model are relatively close to one another. In all specimens, the error percentage of experimental and predicted values during 80 and 100 days of water soaking varies very little (less than 1%). The study proposes CS-ANN (Cuckoo Search-Artificial Neural Network) architecture to predict mass loss. Test results prove that the CS-ANN predicted values are much closer to the experimental results. Cuckoo Search Algorithm (CSA) is used along with the ANN model to optimize and fine-tune the hyperparameters according to the data. The loss curves substantially prove the proposed model to be the best fit for the experimental data.