Experimental and Machine Learning‐Based Process Optimization of UDCF / PA11 Composite Laminates for Hydrogen Storage Applications

This study presents a comprehensive approach to optimizing key processing parameters using the vacuum‐bag‐only hand lay‐up method for unidirectional carbon fiber‐reinforced polyamide 11 laminates intended for novel lightweight hydrogen pressure vessels. The influence of processing parameters: consolidation temperature (200°C–300°C), dwell time (15–60 min), and cooling rate (slow, medium or fast) on mechanical behavior was systematically investigated through experimental multiscale analysis. All tensile tests were conducted under a loading condition perpendicular (90°) to the fiber orientation in the composite matrix to investigate the effect of process parameters on the quality of fiber/matrix adhesion. The measured cross‐sectional void area fraction decreased from 3.53% at 200°C to 0.25% at 210°C and 0.02% at 220°C–230°C, demonstrating markedly improved consolidation. Consequently, structural defects are significantly reduced at 220°C and 230°C, supporting the superior tensile strength and modulus obtained from quasi‐static tests in this window. Additionally, machine learning models were employed to predict optimal fabrication conditions. The combined experimental and AI‐assisted analysis identified 225°C, 30 min of dwell time, and medium cooling rate as the optimal processing parameters providing a favorable balance between mechanical performance and microstructural integrity. Experimental validation at the identified optimal processing conditions demonstrated close agreement between the predicted and measured values, with prediction accuracies of 94.9%, 93.2%, and 96.8% for ultimate tensile strength, elongation at break, and Young's modulus, respectively. These findings highlight the potential of AI‐assisted process optimization to enhance the manufacturing efficiency for sustainable hydrogen storage applications.