We present a novel AI-assisted method for decomposing (segmenting) planar CAD (computer-aided design) models into well shaped rectangular blocks as a proof-of-principle of a general decomposition method applicable to complex 2D and 3D CAD models. The decomposed blocks are required for generating good quality meshes (tilings of quadrilaterals or hexahedra) suitable for numerical simulations of physical systems governed by conservation laws. The problem of hexahedral mesh generation of general CAD models has vexed researchers for over 3 decades and analysts often spend more than 50% of the design-analysis cycle time decomposing complex models into simpler parts meshable by existing techniques. Our method uses reinforcement learning to train an agent to perform a series of optimal cuts on the CAD model that result in a good quality block decomposition. We show that the agent quickly learns an effective strategy for picking the location and direction of the cuts and maximizing its rewards as opposed to making random cuts. This paper is the first successful demonstration of an agent autonomously learning how to perform this block decomposition task effectively thereby holding the promise of a viable method to automate this challenging process.

翻译：我们提出了一种新颖的AI辅助方法，用于将平面CAD（计算机辅助设计）模型分解（分割）为形状良好的矩形块，作为适用于复杂二维和三维CAD模型的通用分解方法的概念验证。这些分解后的块需要用于生成高质量网格（四边形或六面体的铺砌），以适用于由守恒定律支配的物理系统的数值模拟。通用CAD模型的六面体网格生成问题已困扰研究人员超过30年，分析人员通常花费超过50%的设计-分析周期时间将复杂模型分解为可通过现有技术进行网格划分的更简单部分。我们的方法利用强化学习训练一个智能体，在CAD模型上执行一系列最优切割，从而产生高质量的块分解。我们证明，与随机切割相比，该智能体能够迅速学会一种有效策略，用于选择切割位置和方向并最大化其奖励。本文首次成功展示了智能体能够自主学会如何有效地执行这一分块分解任务，从而为自动化这一具有挑战性的过程提供了一种可行方法的前景。