Engineering Commons^™

Moisture Content Prediction In Polymer Composites Using Machine Learning Techniques, Partha Pratim Das, Monjur Morshed Rabby, Vamsee Vadlamudi, Rassel Raihan

Institute of Predictive Performance Methodologies (IPPM-UTARI)

The principal objective of this study is to employ non-destructive broadband dielectric spectroscopy/impedance spectroscopy and machine learning techniques to estimate the moisture content in FRP composites under hygrothermal aging. Here, classification and regression machine learning models that can accurately predict the current moisture saturation state are developed using the frequency domain dielectric response of the composite, in conjunction with the time domain hygrothermal aging effect. First, to categorize the composites based on the present state of the absorbed moisture supervised classification learning models (i.e., quadratic discriminant analysis (QDA), support vector machine (SVM), and artificial neural network-based multilayer perceptron (MLP) classifier) …