review · Heliyon
Machine learning techniques are increasingly being applied to analyse the mechanical behaviour of laminated fibre-reinforced polymer composites. Once trained, these computational models achieve high predictive accuracy while substantially reducing computational costs, making them valuable for in-depth analysis and design optimisation. An evaluation of recent literature demonstrates diverse implementations of machine learning algorithms across composite materials research. These studies show that computational tools can effectively forecast and enhance composite mechanical properties. However, selecting the correct algorithm and designing an appropriate neural network architecture remain critical considerations that depend strictly on the specific problem and the nature of the data involved. Although current findings demonstrate strong capabilities, additional research is required to fully exploit machine learning across the composite materials domain.
Fibre-reinforced polymers are vital structural materials, but simulating and testing their mechanical properties can be computationally demanding. Applying machine learning provides an accurate and faster method to evaluate and improve composite materials. Understanding the strengths and selection criteria for different algorithms helps researchers refine how these materials are designed, paving the way for more efficient engineering workflows.
This work relates to the design and optimisation of fibre-reinforced polymer composites, relevant to engineers and material designers seeking faster property predictions. Based on the review of recent studies, the technology sits at an early to intermediate research stage, where models show high analytical accuracy in tests, but require careful matching of algorithms to specific data sets before widespread, robust commercial implementation can be achieved.
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The utilization of Machine Learning (ML) techniques in the analysis of the mechanical behavior of fiber-reinforced polymers (FRP) has been increasingly applied in composite materials. The ability to achieve high levels of accuracy, coupled with a reduction in computational cost once the ML models are trained, presents a powerful tool for optimization and in-depth analysis of laminated FRP. This review paper aims to provide insight into the emergence of this trend, offer an overview of various ML algorithms and related subtopics, and demonstrate different implementations of ML from recent studies with a specific focus on the design and optimization of FRP composites. The reviewed studies have exhibited high levels of prediction accuracy and have effectively employed ML to optimize the mechanical properties of composite materials. It was also highlighted that selecting the appropriate ML algorithm and neural network structure is crucial for various problems and data. While the studies reviewed have shown promising results, further research is needed to fully realize the potential of ML in this field.
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DOI: 10.1016/j.heliyon.2024.e33681
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