Kuehsamy, S. J., Zhou, H., Wang, Z.-P., & Rosen, D. W. (2024). Designing Non-periodic 3D Woven Composite Preforms Using LSTM Deep Learning Networks. In Computational and Experimental Simulations in Engineering (pp. 540–550). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-68775-4_42
Abstract:
Advanced 3D woven composites show significant promise for use in high-performance applications. Nonetheless, automating the design process for customized non-periodic woven architectures presents challenges due to the complexities arising from large-scale combinatorial design issues. This often leads to inefficient utilization of the tow reinforcement. In this work, by treating the woven design problem as a multi-agent system, we leverage an integrated framework combining the Hungarian Algorithm and Long Short-Term Memory (LSTM) networks. This allows for effective alignment of woven tows along loading paths, while enabling considerations of through-thickness reinforcement.
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Funding Info:
This research / project is supported by the Agency for Science, Technology and Research (A*STAR) - Career Development Fund
Grant Reference no. : C210112026
This research / project is supported by the Agency for Science, Technology and Research (A*STAR) - Start-up Fund
Grant Reference no. :