A new partitioning process for geometrical product specifications and verification
A new partitioning process for geometrical product specifications and verification
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DOI：
10.1016/j.precisioneng.2019.12.009
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发表时间：
2020-03-01
影响因子：
3.6
通讯作者：
Jiang, Xiangqian
中科院分区：
文献类型：
--
作者：
Cai, Na;Anwer, Nabil;Jiang, Xiangqian
在ISO产品几何技术规范和验证标准（GPS）中，特征操作用于获得理想和非理想特征。这些操作的形式化能够减少机械产品的设计，制造和计量活动中的模糊性和不确定性，并且它们的科学研究有助于开发一个健全的数学框架和形式主义，用于理解工程实践和制定新标准。划分是ISO GPS标准中定义的一种基本操作，其目的是将零件分解为独立的特征或表面部分，以供进一步处理和分析。在本文中，一个国家的最先进的调查的分割和分割方法和技术在文献中报道的进行，并提出了一个全面的分类。此后，开发了一个新的分区过程，用于划分成区域，并将每个区域识别为七个不变类的表面之一。它进行了三个主要步骤：初始分区的基础上的形状指数和曲率，精细分区的滑动分析和不变性类识别的统计评估。一个直观的形状色轮被定义为可视化的分区功能，根据其相应的不变性类。在标称模型和实测点云上的实验结果验证了该方法的有效性。
In ISO Geometrical Product Specifications and Verification Standards (GPS), Feature operations are used to obtain ideal and non-ideal features. The formalization of such operations enables to reduce ambiguity and uncertainty within the activities of design, manufacture and metrology of mechanical products, and their scientific investigation contributes to develop a sound mathematical framework and formalisms for the comprehension of engineering practices and the development of new standards. Partitioning is a fundamental operation defined in ISO GPS standard which aims at decomposing a part into independent features or surface portions for further processing and analysis. In this paper, a state-of-the-art survey of partitioning and segmentation methods and techniques reported in the literature is conducted and a comprehensive classification is proposed. Thereafter, a new partitioning process is developed for partitioning into regions and recognizing each region as one of the seven invariance classes of surfaces. It proceeds in three main steps: initial partitioning based on shape index and curvedness, refined partitioning by slippage analysis and invariance class recognition by statistical evaluation. An intuitive shape color wheel is defined to visualize the partitioned features according to their corresponding invariance classes. Experiments and results on both nominal models and measured point clouds are presented to demonstrate the effectiveness of the proposed method.