1 - College of Computer Science and Technology, Zhejiang University, Hangzhou, 310007, China

2 - Zhejiang Sci-Tech University, Hangzhou, 310018, China

3 - Shenzhen Poisson Software Co., Ltd., Shenzhen, 518129, China

* - Corresponding Author

Abstract

This paper addresses the challenge of efficiently abstracting mid-surfaces from complex variable thin-walled models, a critical task in computer-aided design (CAD) and finite element analysis (FEA) for simplifying thin-walled structures. Traditional methods often require manual specification of pairing faces, which can be time-consuming and error-prone. Alternatively, automatic face pairing methods fail to meet the actual needs of variable thin-walled models, resulting in the accumulation of topological errors. Additionally, existing algorithms struggle to extract mid-surfaces from models with varying wall thickness or produce mid-surfaces with poor accuracy, leading to geometric errors. Furthermore, the computational efficiency of these methods is often inadequate for large-scale models. To overcome these challenges, we propose an automated face-pairing mechanism that eliminates the need for manual intervention, enhancing the algorithm’s robustness and enabling it to handle cases that the commercial CAD modeling engine, Parasolid, cannot process. Our approach accurately processes variable thin-walled models, with results closely aligning with the ground truth, as demonstrated by the provided error distribution tables. Moreover, our algorithm achieves a 4 − 12 times improvement in efficiency than previous methods over the geometry extraction stage and supports multi-threaded acceleration, significantly reducing computation time. Experimental results demonstrate that our algorithm surpasses existing methods in both accuracy and efficiency, offering a promising solution for mid-surface extraction in complex, variable thin-walled models.
 

Overview: The input thin-walled model’s faces are first classified into different types and further organized into distinct face group pairs (FGPs). Subsequently, the mid-surface geometry extraction algorithm extracts the mid-surface for each face group based on the wall thickness. Finally, the model undergoes trimming operations, including intersection and imprinting, to determine the boundaries of the mid-surface, thereby yielding the final mid-surface.

Benchmark:

• Model 1: A plastic thin-shell model with constant wall thickness, consisting of 37 faces and no multi-face pairing scenarios, used to validate the algorithm’s basic capabilities.
• Model 2: A ceramic bowl with 17 faces, including 8 variable-thickness face pairs. This model represents a simple case with only 1-1 or 1-n face pairs.
• Model 3: A turbo impeller, comprising 91 faces, most of which involve 1-n or n-n pair faces. The freeform surfaces exhibit more complex variations, challenging the algorithm’s handling of intricate geometry.
• Model 4: The original version of Model 1 without feature suppression, containing both constant and variable wall thickness scenarios with 78 faces. It better represents the true mid-surface of Model 1.
• Model 5: A tire model from the automotive industry, featuring 167 faces, including 28 variable-wall thickness face pairs and 27 n-n face pairs. Its complex topology and diverse variable wall thickness scenarios pose significant challenges.
• Model 6: An aircraft model from a real-world aerospace scenario, with 286 faces and highly complex topological and geometric structures, used to verify the algorithm’s robustness under the most challenging conditions.

Contents

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Acknowledgements

This work was funded in part by "Pioneer" and "Leading Goose" R&D Program of Zhejiang Province (No. 2025C01086).

tang_m@zju.edu.cn