Processing parameter optimization for Carbon Fibre-Reinforced Plastics (CFRPs) using Wet Compression Molding (WCM) – A statistical approach

Wet Compression Molding (WCM) is a rapid and efficient manufacturing process within the broader Liquid Composite Molding (LCM) family, used to produce continuous fiber-reinforced polymer (CFRPs) composites for automotive lightweighting and performance-critical applications. Part quality in WCM is strongly dependent on the precise selection of processing parameters to ensure complete curing, optimal fiber wetting, and consistent mechanical performance. This study expanded the WCM parameter set to include resin temperature, mold temperature, resin set time, cure time, optical and thermal imaging analysis, demolding characterization, and pressure-profile evaluation across a unique operating range. Statistical analysis employing multi-factor regression, Analysis of Variance (ANOVA), General Linear Model (GLM), and multivariate comparisons was conducted to determine correlations between initially wetted and secondary wetted regions, flexural properties, thermal behavior, and demolding outcomes. The parametric study incorporated initial resin application and inertial effects from an automated 6-axis ABB (IRB 6700-205/2.8) robot, with experiments performed at the Fraunhofer Innovation Platform (FIP) in London, Ontario, to accelerate product development and reduce resource-intensive testing. Automation of the WCM process substantially reduced variability and cycle time, while identification of optimal processing parameters, a mold temperature of 110 °C, resin temperature of 50 °C, a resin set time of 0 s and mold curing time of 300 s, ensured complete curing, enhanced fiber wetting, and produced CFRP plaques with consistently high flexural strength and strain at break. Thermal analysis demonstrated that pre-cure temperature fluctuations, insufficient consolidation pressures, and improper demolding directly reduced mechanical performance, emphasizing the need for precise mold temperature regulation, maintaining pressures above 380 kPa, and controlled demolding to minimize void formation. Optical microscopy confirmed that regions with lower void content corresponded to higher Young's modulus values, highlighting the impact of microstructural quality on mechanical behavior, and subsequent micro-computed tomography scans are recommended to further refine predictive models. By integrating statistical modeling with thermal, pressure, and demolding analyses, the study establishes a predictive framework that reduces experimental effort and supports automated, data-driven WCM process optimization. The methodology enables rapid evaluation of new resin profiles and process variations, typically within 1 h, providing actionable guidance for resin flow, cure conditions, and mechanical property control. These findings advance the adoption of Industry 4.0 practices in composite manufacturing, improve production efficiency, and reduce energy and material waste. The study demonstrates a scalable approach to producing high-quality, durable CFRP components while accelerating design, validation, and deployment of WCM systems in both academic and industrial settings.