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Automatic fixtureless inspection of non-rigid parts based on filtering registration points

机译:根据过滤注册点自动对非刚性零件进行无夹具检查

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摘要

Computer-aided inspection (CAI) of non-rigid parts significantly contributes to improving performance of products, reducing assembly time and decreasing production costs. CAI methods use scanners to measure point clouds on parts and compare them with the nominal computer-aided design (CAD) model. Due to the compliance of non-rigid parts and for inspection in supplier and client facilities, two sets of sophisticated and expensive dedicated fixtures are usually required to compensate for the deformation of these parts during inspection. CAI methods for fixtureless inspection of non-rigid parts aim at scanning these parts in a free-state for which one of the main challenges is to distinguish between possible geometric deviation (defects) and flexible deformation associated with free-state. In this work, the generalized inspection fixture (GNIF) method is applied to generate a prior set of corresponding sample points between CAD and scanned models. These points are used to deform the CAD model to the scanned model via finite element non-rigid registration. Then, defects are identified by comparing the deformed CAD model with the scanned model. The fact that some sample points can be located close to defects results in an inaccurate estimation of these defects. In this paper, a method is introduced to automatically filter out sample points that are close to defects. This method is based on curvature and von Mises stress. Once filtered, the remaining sample points are used in a new registration, which allows identifying and quantifying defects more accurately. The proposed method is validated on aerospace parts.
机译:非刚性零件的计算机辅助检查(CAI)大大有助于提高产品性能,减少组装时间并降低生产成本。 CAI方法使用扫描仪来测量零件上的点云,并将其与标称计算机辅助设计(CAD)模型进行比较。由于符合非刚性零件的要求以及在供应商和客户设施中进行检查的要求,通常需要使用两组昂贵的精密专用夹具来补偿这些零件在检查过程中的变形。用于非刚性零件的无夹具检查的CAI方法旨在在自由状态下扫描这些零件,其主要挑战之一是区分可能的几何偏差(缺陷)和与自由状态相关的柔性变形。在这项工作中,应用了通用检查夹具(GNIF)方法来生成CAD和扫描模型之间的一组先前的对应采样点。这些点用于通过有限元非刚性配准将CAD模型变形为扫描模型。然后,通过将变形的CAD模型与扫描的模型进行比较来识别缺陷。某些采样点可能位于缺陷附近的事实导致对这些缺陷的估计不准确。在本文中,介绍了一种自动过滤掉接近缺陷的采样点的方法。该方法基于曲率和冯·米塞斯应力。过滤后,剩余的采样点将用于新的配准中,从而可以更准确地识别和量化缺陷。该方法在航空航天零件上得到了验证。

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