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A shape sampling technique via particle tracing for CAD models

机译：用于CAD模型的通过粒子跟踪的形状采样技术

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摘要

In this paper, a shape sampling approach is proposed for CAD products that can be used to suggest innovative product shapes to designers and consumers. These shapes are intended to inspire designers and can be employed during the design process. For a given set of geometric parameters defining the product shapes, parameter relationships (i.e., geometric constraints), and parameter ranges, a particle tracing (PT) algorithm is proposed to find product shapes that satisfy the defined geometric constraints in the shape space. Particles are placed at points in the shape space by minimizing the Audze-Eglais potential energy of the particle positions using a permutation genetic algorithm. They then move until one of the predetermined stopping criteria is met. Particle movement is achieved using a cost function that favors movement towards feasible shapes. By iteratively running the PT algorithm, feasible shapes are obtained. Representatives of these shapes are identified using a kmedoids clustering approach, and such representatives can be used by designers or shown to consumers to customize the product according to their preferences. In this paper, eight CAD models (e.g., car hood, yacht hull, wheel rim) are utilized to validate the performance of the proposed sampling technique. We also compare our technique with related methods.
机译：本文提出了一种针对CAD产品的形状采样方法，该方法可用于向设计师和消费者建议创新的产品形状。这些形状旨在激发设计人员的灵感，并可以在设计过程中使用。对于给定的一组定义产品形状，参数关系（即几何约束）和参数范围的几何参数，提出了一种粒子跟踪（PT）算法来查找在形状空间中满足定义的几何约束的产品形状。使用置换遗传算法通过最小化粒子位置的Audze-Eglais势能，将粒子放置在形状空间中的点上。然后它们移动直到满足预定的停止标准之一。使用有利于向可行形状运动的成本函数可以实现粒子运动。通过迭代运行PT算法，可以获得可行的形状。这些形状的代表使用kmedoids聚类方法进行识别，设计师可以使用这些代表，也可以向消费者展示这些代表，以根据他们的喜好定制产品。在本文中，八个CAD模型（例如汽车引擎盖，游艇船体，轮辋）被用来验证所提出的采样技术的性能。我们还将我们的技术与相关方法进行了比较。

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