Fast and accurate prediction of cure quality and mechanical performance in fiber‐reinforced polymer composite using dielectric variables and machine learning

Abstract

AbstractFiber‐reinforced polymer (FRP) composites are widely used in the aerospace and automotive industries due to their high strength‐to‐weight ratio. However, the manufacturing process of FRP composites is intricate, requiring precise control over multiple parameters, which can be challenging. To ensure top‐notch product quality and bolster confidence in the durability of composite materials, it requires a uniform curing evaluation technique and a predictive strength model. This study presents a rapid nondestructive quality inspection technique for composites, involving three distinct studies that employ machine learning techniques in combination with the frequency‐dependent dielectric response of materials. The first study introduces a nondestructive method for swiftly inspecting the cure state (degree of cure) of composite samples. This technique combines broadband dielectric spectroscopy with supervised machine learning algorithms, particularly support vector machines. The second study employs advanced artificial neural networks like multi‐layer perceptron to predict the tensile strength, a measure of mechanical performance, of composite materials. The results demonstrate an accurate classification of the curing state with 96.7% accuracy and a prediction of tensile strength with 87.5% accuracy. The final study explores the application of machine learning in quality monitoring of prepreg (raw materials for FRP) aging at room temperature.Highlights Integration of machine learning with frequency‐dependent dielectric measurement offers efficient method for inspecting and monitoring the curing state and mechanical performance of composite materials. Machine learning and frequency‐dependent dielectric response used to accurately assess the curing state of fiber‐reinforced polymer composites with 96.7% accuracy, which saves the time and effort and also reduces the need for destructive testing. Advanced artificial neural networks, specifically multi‐layer perceptron, employed to predict mechanical performance of composite materials with 87.5% accuracy, enhancing confidence in product reliability. Novel dielectric measurement technique integrated with machine learning algorithms enables prepreg age monitoring at room temperature.