The influence of fabric architecture on impregnation behavior and void formation: Artificial neural network and statistical‐based analysis

Abstract

This work proposes an approach combining artificial neural networks (ANN) with statistical models to predict injection processing conditions for four reinforcement architectures: plain weave, bidirectional noncrimp fabrics, unidirectional fabrics (Uni) and random fiber mats (Random). Key results allow evaluating the velocity of the flow front by combining processing parameters and creating a three‐dimensional response surface based on a properly trained ANN. This investigation is based on a large number of experimental results. The key role played by some physical parameters was associated with predicting the impregnation behavior (velocity of the flow front) during resin injection. The main outcome aims to provide a better control of void content in terms of size and position to the four fibrous reinforcements considered.

Effect of meso‐structure characteristics on surface emissivity of 2.5D Woven ablative composite for thermal protection

“…[20] This phenomenon indicated that resin-based woven ablative composite becomes highly porous near the ablation surface, thus meso-structure and surface appearance have significant effects on radiation. Some researchers investigate the mesoscale braided architectures influence on the impregnation behavior, void formation, [21] and mechanical properties of composites. [22][23][24] Some researchers [25,26] simulated radiative properties in fiber tows or in woven composites with different arrangement of fiber bundles.…”

Determination of needling process satisfying stiffness requirements of 3D needled composites

“…Recently, some surrogate models, such as artificial neural networks [15][16][17][18][19][20] combined with support vector machine [21] and clustering analysis [22][23][24][25] have been proposed to design process parameters and predict physical properties. These models have some advantages of short running time and accurate prediction results.…”