Natural Fiber Composites: A Comprehensive Review on Machine Learning Methods

Abstract

Composites materials reinforced with natural fibers are currently gaining traction in many industries including automotive, aerospace, marine, packaging and construction due to their ecological consciousness and high strength to weight ratio. To enhance the overall performance and use of natural fibers composites (NFC) in different industries, it is crucial to understand their acoustic properties, moisture absorption, mechanical characteristics, manufacturing processes, tribological behavior and damage mechanics. Analyzing the performance of NFC is a complex process due to the heterogeneity and anisotropic nature of NFC coupled with their susceptibility to environmental factors that lead to a significant variability in their composites. Research on NFC performance typically depends on the time consuming and costly experiments with limited reproducibility and computationally intensive simulations. Machine learning (ML) techniques can efficiently uncover data patterns and offer high reproducibility. Additionally, advancements in NFC manufacturing and testing have produced vast amounts of data. The current review not only discusses the application of ML methods in enhancing NFC performance, but also identifies the challenges and opportunities associated with using ML in NFC research. By utilizing ML methods, NFC limitations can be overcome, leading to improved performance.