2025 ASEE Annual Conference & Exposition

Artificial intelligence is becoming a powerful tool to design and develop new materials, especially in composite materials design. Fiber-reinforced polymer composites have an exceptional advantage over traditional materials for their superior or specific stiffness and strength, and resistance to corrosion and fatigue, which results in low total lifetime cost. One of the primary limitations of these composite materials is predicting mechanical, thermal, and electrical properties due to the anisotropy of the materials. Predicting mechanical, thermal, and electrical properties is important in utilizing these composite materials for multiple engineering applications. To address this issue requires a machine learning model trained from large composite materials structure property and performance relationship. In recent years artificial intelligence and machine learning have shown substantial interest in predicting different properties of composite materials. More exploration needs to be required to predict the mechanical, thermal, and electrical properties of composite materials using artificial intelligence and machine learning. Firstly, this research aims to develop a new data-driven computational method to design and analyze fiber-reinforced composite materials. Secondly, to design and develop artificial intelligence and machine learning courses for undergraduate mechanical engineering technology students.
Keywords: Machine Learning, Property Prediction, Composite Materials