Predicting Mechanical Properties of Glass-Epoxy Composites Using Supervised Learning

Abstract

The traditional testing methods for evaluating mechanical properties of composite laminates such as Interlaminar Shear Strength (ILSS) and In-Plane Shear Strength are known to be resource-intensive, time-consuming, and expensive. This often leads to setbacks and failures in the development process. In this study, the development of a predictive model was proposed to estimate these key mechanical properties based on specific test measurements and dimensions. The glass-epoxy family of composites was focused on as it is widely used in various industries. To explore the feasibility of this approach, supervised machine learning techniques were employed, which offer an efficient means to create predictive models based on test features. The selected tests for consideration include the Short Beam Strength Test (SBS Test) and Iosipescu or V–notch test for ILSS and in–plane shear strength respectively.

The performance of different machine learning algorithms such as decision tree, multiple linear regres- sion, ridge regression, and artificial neural networks was evaluated to identify the most suitable model for the dataset. Given the limited availability of data, the study emphasizes the importance of achieving good performance even with small datasets. The findings from this research hold promise for streamlin- ing the testing process and improving the efficiency of composite material development.