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Integrating Deep Learning with Generative Design and Topology Optimization for Efficient Additive Manufacturing
- 1 Meiji University, Tokyo, Japan
* Author to whom correspondence should be addressed.
Abstract
Additive manufacturing (AM) through generative design and topology optimisation creates complex, lightweight structures with exceptional material efficiency and structural integrity. When coupled with deep learning functionality, generative design and topology optimisation can explore broader design spaces and optimise more efficiently, creating novel AM structures that utilise material more efficiently and have better strength and performance than their counterparts created through conventional AM methods. The study tackles how deep learning models such as convolutional neural networks (CNNs) can be integrated into generative design and topology optimisation and how these integration help optimise material usage, production time and performance. Case studies from the aerospace, automotive, and healthcare industries exemplify how these synergies resulted in more resilient, cost-effective designs that would not have been possible through conventional AM approaches. The study focuses on material usage efficiency, reduction in production time and performance improvement to showcase how deep learning integrations enhance the process from design conceptualisation, through iterations, to final production.
Keywords
Generative Design, Topology Optimization, Deep Learning, Additive Manufacturing, Material Efficiency.
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Cite this article
Huang,L. (2024). Integrating Deep Learning with Generative Design and Topology Optimization for Efficient Additive Manufacturing. Applied and Computational Engineering,116,55-60.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 5th International Conference on Signal Processing and Machine Learning
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