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 to predict face pair confidence from geometric and topological features, handling complex pairing scenarios beyond rule-based methods; and (2) an interference implicit field that represents mid-surfaces as the interference of two signed distance functions, enabling generalized offset control for flexible positioning in downstream CAE/FEA-oriented workflows. We construct a large-scale mid-surface dataset containing over 1,500 manually annotated CAD models. Experiments demonstrate that MidSurfNet achieves 87.32% face pairing accuracy and successfully handles multi-wall-thickness (61.90% completion) and self-matching (52.94% completion) scenarios that confound all existing methods. Furthermore, MidSurfNet provides a learning-based approach to generalized mid-surface abstraction with arbitrary offset control for CAE-oriented applications.

翻译：中面抽象对于薄壁CAD模型的有限元分析至关重要。现有基于面对配对的方法依赖手工设计的几何启发式规则，但实际工业模型常呈现多壁厚区域、自匹配面对构型及非中心偏移曲面需求——这些场景下基于规则的方法普遍失效。我们提出MidSurfNet，一个通过学习增强的框架，通过两个新颖组件解决这些局限：（1）神经面对配对模块，从几何与拓扑特征中学习预测面对配对置信度，处理超越规则方法的复杂配对场景；（2）干涉隐式场，将中面表示为两个符号距离函数的干涉，实现通用偏移控制以支持下游CAE/FEA导向工作流中的灵活定位。我们构建了一个包含超1500个手工标注CAD模型的大规模中面数据集。实验表明，MidSurfNet达到87.32%的面对配对准确率，并能成功处理所有现有方法均无法应对的多壁厚场景（完成率61.90%）和自匹配场景（完成率52.94%）。此外，MidSurfNet为面向CAE应用的任意偏移控制通用中面抽象提供了基于学习的解决方案。