Experimental and AI-assisted optimization of mechanical performance of hybrid flax–basalt composite laminates

The research aims to bridge the gap by providing an integrated approach to material design, process optimization, and intelligent failure prediction. A novel approach to material design and process optimization was developed to examine the impact of stacking sequences and processing parameters on the mechanical behavior of flax-basalt composite laminates. Five laminate stacks were manufactured by hand lay-up and compression curing techniques. These stacks were then characterized by tensile, flexural, impact, interlaminar fracture toughness (ILFT), and energy absorption density (EAD) properties. Among the laminate stacks, B-F-F-F-B (S2) showed superior properties with tensile, flexural, impact, ILFT, and EAD properties of 150 MPa, 185 MPa, 14.2 J, 1580 J/m 2 , and 0.68 J/cm 3 , respectively. To further optimize the material properties, the Response Surface Methodology (RSM) was combined with a novel approach based on a hybrid DQN-LSTM-Mother Optimization Algorithm (MOA) approach. In addition, scanning electron microscope (SEM) analysis was incorporated to interpret the material properties and provide a direct link to material structure and properties. This approach represents a major advancement over existing RSM-ANN and machine learning techniques for designing high-performance composite materials.