arXiv:2606.19921v1 Announce Type: new Abstract: This work proposes an element-based Convolutional Neural Network (CNN) to accelerate density-based Topology Optimization (TO), termed eCNNTO. TO generally undergoes a large number of iterations, where finite element analysis is performed in every iteration, leading to the efficiency bottleneck especially when dense meshes are used to achieve high-resolution designs. To address this limitation, eCNNTO is proposed to build upon Kallioras et al. (2020), where a Deep Belief Network (DBN) was trained for every element to predict its near-optimal densi
The continuous drive for efficiency in engineering design and the increasing maturity of AI/ML techniques on edge computing make this type of AI acceleration pertinent.
Accelerating topology optimization significantly reduces design cycles for complex structures, offering competitive advantages in fields like aerospace, automotive, and advanced manufacturing.
The computational bottleneck in topology optimization, especially for high-resolution designs, is diminished, enabling faster iteration and more complex product development.
- · Aerospace Industry
- · Automotive Industry
- · Advanced Manufacturing
- · AI/ML in Engineering Software
- · Traditional high-performance computing in design
- · Companies without AI integration in design
Faster design-to-production cycles for optimized components across various industries.
Increased complexity and performance in manufactured goods due to more exhaustive design exploration.
A potential shift in the skillset required for design engineers, emphasizing AI proficiency over brute-force simulation expertise.
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Read at arXiv cs.AI