Efficiently registering scan point clouds of 3D printed parts for shape accuracy assessment and modeling
Efficiently registering scan point clouds of 3D printed parts for shape accuracy assessment and modeling
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DOI：
10.1016/j.jmsy.2020.04.001
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发表时间：
2020-07-01
影响因子：
12.1
通讯作者：
Huang, Qiang
中科院分区：
文献类型：
--
作者：
Decker, Nathan;Wang, Yuanxiang;Huang, Qiang
评估增材制造（AM）生产的零件与其预期设计之间的几何差异的一种流行方法是使用3D扫描仪来产生点云。然后将该数字扫描与零件的预期设计对齐，以量化打印精度。实现这种对齐的最常见方法之一是迭代最近点（ICP）算法。本文评估了几个潜在的陷阱，可以遇到时，应用ICP评估AM零件的尺寸精度。这些挑战，然后说明使用模拟数据，允许量化其对偏差测量的准确性的影响。这些配准误差中的每一个都被证明是显著的，足以显著地影响测量的偏差。然后提出了一种有效和实用的方法来解决这些错误的基础上工程知情的假设。所提出的方法和传统的无约束ICP用于产生对齐的真实的和模拟的测量数据。采用线性混合效应建模方法，进行了真实的设计实验，以比较两种配准方法获得的结果。所提出的方法产生的路线，不太敏感的变化源，并产生偏差测量，不会低估真正的形状偏差的无约束ICP算法通常没有。
One popular approach to assess the geometric differences between a part produced by additive manufacturing (AM) and its intended design is the use of a 3D scanner to produce a point cloud. This digital scan is then aligned against the part's intended design, allowing for quantification of print accuracy. One of the most common methods for achieving this alignment is the Iterative Closest Point (ICP) algorithm. This paper evaluates several potential pitfalls that can be encountered when applying ICP for assessment of dimensional accuracy of AM parts. These challenges are then illustrated using simulated data, allowing for quantification of their impact on the accuracy of deviation measurements. Each of these registration errors was shown to be significant enough to noticeably affect the measured deviations. An efficient and practical method to address several of these errors based on engineering informed assumptions is then presented. Both the proposed method and traditional unconstrained ICP are used to produce alignments of real and simulated measurement data. A real designed experiment was conducted to compare the results obtained by the two registration methods using a linear mixed effects modeling approach. The proposed method is shown to produce alignments that were less sensitive to variation sources, and to generate deviation measurements that will not underestimate the true shape deviations as the unconstrained ICP algorithm commonly does.