Applying Hybrid Deep Neural Networks With Manually Derived Layers to Model Compression Strength of Angled CFRP in Aerospace Industry
Year: 2025
Type: conference-paper
Abstract: This study models the impact of ply orientation and stacking sequence on open-hole compression strength of aerospace-grade composite laminates. Innovating from traditional modeling techniques, this study combines physics-based analytical solutions with data-driven methods to study composite structures. The proposed hybrid model incorporates interply compatibility theory as a manually derived hidden layer in the Multilayer Perceptron architecture, and achieves root mean square error (RMSE) of 20.1 MPa (6.2% average failure strength) and R2 of 0.85 on experimental test records. The hybrid model outperforms either physics-based analytical model or data-driven models when used individually. In addition, it offers enhanced physical interpretation by validating the importance of interply compatibility in laminate failure. The success in modeling non-standard ply angles, not just (0°, ±45°, 90°), and multiple stacking sequence patterns unlocks the potential of designing composite laminates with a much larger design space. The proposed model could facilitate stacking sequence optimization to maximize strength for given laminate compositions.
Authors: Su Yu, Jonathan S. Colton
Institution: Georgia Institute of Technology
Language: English
Subfield: Mechanics of Materials
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4414643406