Advancements in polymeric matrix composite production: a review on methods and approaches

Year 2024, Volume: 8 Issue: 4, 677 - 686, 31.10.2024

Zeynep Soydan , Fatma İrem Şahin , Nil Acaralı

https://doi.org/10.31127/tuje.1468998

Abstract

This study focused on the comprehensive exploration of composite materials, elucidating their properties, and classifying them based on matrix materials. Emphasis was placed on thermoplastic matrix composite production methods, shedding light on their properties. An extensive examination of various production processes, ranging from traditional methods to cutting-edge technologies like automatic fiber placement and additive manufacturing were undertaken. The study extensively examined various production methods for thermoplastic matrix composites, discussing the advantages, disadvantages, and optimal characteristics of each technique. Thermoplastic matrix composite production processes encompassed such as hand lay-up, spray-up, filament winding, vacuum bag molding, vacuum infusion, resin transfer molding, compression molding, pultrusion, injection molding, centrifugal casting and lamination were discussed. While composite materials offered corrosion protection, high temperature resistance, and electrical stability, challenges including costly production, intricated repair processes, and short shelf life persist. Despite the popularity of thermoset matrix composites, the study underscores the need for more efficient thermoplastic composite production methods, addressing emerging trends and digital transformations reshaping the landscape of composite manufacturing. Anticipating the integration of machine learning algorithms for optimizing parameters, the study foresaw a future where composite production processes become significantly more efficient and comprehensive. The review was underscored the transformative impact of machine learning and process modelling on optimization studies, paving the way for more efficient and comprehensive composite manufacturing.

Keywords

Thermoplastic matrix, Composite, Machine learning, Fiber

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