Process–structure–property optimization of carbon fiber-reinforced polyetheretherketone composites manufactured via high-temperature automated fiber placement techniques

Olowonigba, Juwon Kehinde (2025) Process–structure–property optimization of carbon fiber-reinforced polyetheretherketone composites manufactured via high-temperature automated fiber placement techniques. World Journal of Advanced Research and Reviews, 27 (2). pp. 851-870. ISSN 2581-9615

Carbon fiber-reinforced polyetheretherketone (CF/PEEK) composites have emerged as a high-performance class of thermoplastic composites offering exceptional strength-to-weight ratios, chemical resistance, and thermal stability. These attributes make CF/PEEK an attractive material for aerospace, automotive, and biomedical applications where durability under extreme service conditions is essential. Among manufacturing methods, high-temperature automated fiber placement (AFP) has gained prominence for producing large, complex structural components with precise control over fiber orientation and minimal material waste. However, the process–structure–property relationship in CF/PEEK systems remains a complex, multi-parameter optimization challenge due to the intricate interactions between processing parameters, microstructural evolution, and final mechanical performance. This study adopts an integrated process–structure–property optimization framework, combining experimental investigation with computational modeling, to address the critical manufacturing variables influencing CF/PEEK performance. Process parameters such as layup speed, consolidation pressure, heat input, and cooling rates are systematically varied to capture their effects on interlaminar bonding, void formation, and crystallinity. Microstructural characterization using microscopy and thermal analysis reveals how fiber alignment, resin distribution, and crystallization kinetics govern stiffness, strength, and impact resistance. Advanced statistical design of experiments (DoE) and machine learning regression models are applied to develop predictive process maps, enabling targeted parameter tuning for specific property requirements. The findings demonstrate that optimal property performance can be achieved through precise thermal control and consolidation strategies, reducing void content below 1% and maximizing crystallinity without inducing thermal degradation. This process–structure–property optimization framework provides a scalable methodology for high-temperature AFP manufacturing of CF/PEEK, ensuring consistent quality and facilitating industrial adoption in safety-critical applications.