MidSurfNet: Learnable Face Pairing and Interference Implicit Fields for Generalized Mid-surface Abstraction
arXiv:2606.01891v1 Announce Type: cross Abstract: Mid-surface abstraction is essential for finite element analysis of thin-walled CAD models. Existing face pairing-based methods rely on handcrafted geometric heuristics, yet real-world industrial models frequently exhibit multi-wall-thickness regions, self-matching face configurations, and demand for non-center offset surfaces--scenarios where rule-based approaches consistently fail. We present MidSurfNet, a learning-augmented framework that addresses these limitations through two novel components: (1) a neural face pairing module that learns t
The increasing complexity of CAD models and the limitations of traditional rule-based methods in engineering analysis necessitate more robust, AI-driven solutions.
Improved mid-surface abstraction through AI can significantly enhance the efficiency and accuracy of finite element analysis, critical for product design and manufacturing in various industries.
The adoption of learning-augmented frameworks will reduce reliance on handcrafted geometric heuristics, enabling more accurate and generalized analysis of complex thin-walled structures.
- · Mechanical Engineering Firms
- · CAD Software Providers
- · Product Design & Manufacturing
- · AI/ML Engineering
- · Traditional CAE Consultancies
- · Manual Geometry Pre-processors
More accurate and faster simulation cycles will lead to accelerated product development.
Reduced design iteration times could lower development costs and bring more complex, optimized products to market.
This could enable a new class of complex, lightweight designs previously too challenging to analyze efficiently, impacting aerospace, automotive, and defence sectors.
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Read at arXiv cs.LG