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

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Abstract
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.

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