Title: Predicting Mechanical Properties of High-Temperature Fiber-Reinforced Composites Using Machine Learning Approaches

Authors: Md Shafinur Murad, Abdulhammed K. Hamzat,Eylem Asmatulu, Ersin Bahçeci, Mete Bakir, Ramazan Asmatulu

DOI: 10.33599/nasampe/c.24.0245

Abstract: Fiber-reinforced composites (FRCs) have emerged as a critical material in various industrial applications such as aerospace, defense, medical, energy, and automotive due to their superior strength-to-weight ratio, corrosion resistance, and versatility. The utilization of composite materials in various engineering disciplines relies heavily on the accurate determination of their mechanical properties. However, there are discrepancies in the actual mechanical properties available for designers and manufacturers because of some fabrication process, such as fiber and matrix types, fiber orientations, and stacking sequences, and curing condition. This study explores the application of various machine learning algorithms as a novel modeling tool for the prediction of mechanical properties of fiber-reinforced high temperature composites. Machine learning models such as Linear Regression (LR), Support Vector Regression (SVR), and Multilayer Perceptron (MLP), have been used to develop the prediction model for the tensile and flexural properties of fiber-reinforced composite. Accordingly, homogeneous sets of data were collected from a series of experiments conducted in our laboratory. Reinforcement and matrix type, ply orientation, fiber and matrix content, and stacking sequence are the attributes employed for mechanical property estimation. The performance of the models was estimated using several statistical parameters such as Mean Absolute Error (MAE), Coefficient of Determination (R2), and Root Mean Square Error (RMSE). The test results indicate that support vector regression exhibits a good prediction accuracy with an R-value of 0.996 for flexural strength prediction, while Linear regression showed superior performance with an R2 value of 0.998 for tensile strength prediction. Hyperparameter optimization was carried out to improve the performance of the implemented models. This model can serve as a robust prediction tool specifically tensile and flexural strength of fiber-reinforced composites, thereby modernizing how these properties are evaluated before choosing for intended engineering applications.

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Conference: CAMX 2024 | San Diego CA

Publication Date: 2024/9/9

SKU: TP24-0000000245

Pages: 10

Price: $20.00