Automatic Feature-Based Point Cloud Alignment and Inspection

Yu, Jin; Liao, Haitao; Pierson, Harry A.

doi:10.1016/j.promfg.2020.01.405

Cited by 2 publications

(5 citation statements)

References 8 publications

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“…A shape profile is a piecewise polynomial function, r ( θ ), estimated based on the polar coordinates (Jin et al , 2019). Given a point ( x , y ) from the point cloud, the corresponding polar coordinates ( θ , r ) can be expressed as: where ( x 0 , y 0 ) is the centroid of the 2 D shape.…”

Section: Methodsmentioning

confidence: 99%

“…Similar alignment procedures can be conducted based only on the segments associated with datum features or a collection of user-defined features. The detail steps and applications are elaborated upon in Jin et al (2019).…”

Section: Methodsmentioning

confidence: 99%

“…The main idea of phase correction is to find the phase shift by representing the original image in the polar coordinate system (PCS). The proposed methods have been initially studied by Jin et al (2019), which can directly align the reference shape to the sample point cloud without conducting a rough pre-alignment or manual intervention. In this work, the alignment idea is advanced by using a multi-resolution alignment methodology based on the wavelet components to verify the alignment consistency under different levels of resolution.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Moreover, in practice, the dimensional accuracy is associated with datum features from a Geometric Dimensioning and Tolerancing (GD&T) perspective, but the least-square fitting methods are not able to automatically align point clouds based on such datum features without first performing a global rough alignment. A phase-correction–based alignment method is proposed by Jin et al (2019) for datum-based alignment. This method is adopted and improved in this work to align a point cloud with different levels of details maintained and to evaluate the alignment performance in terms of alignment error and consistency.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, a two-stage optimization model is proposed to optimize the efficiency and effectiveness of the proposed framework. This work mainly focuses on illustrating the multi-resolution framework; therefore, the procedure of datum-based alignment, which has been elaborated upon in Jin et al (2019), will not be repeated in this work. Three layers of a 3D printed human heart are considered as examples to demonstrate the effectiveness of proposed methods.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A multi-resolution framework for automated in-plane alignment and error quantification in additive manufacturing

Jin

Liao

Pierson

2020

RPJ

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Purpose Additive manufacturing (AM) has shown its capability in producing complex geometries. Due to the additive nature, the in situ layer-wise inspection of geometric accuracy is essential to making AM reach its full potential. This paper aims to propose a novel automated in-plane alignment and error quantification framework to distinguish the fabrication, measurement and alignment errors in AM. Design/methodology/approach In this work, a multi-resolution framework based on wavelet decomposition is proposed to automatically align two-dimensional point clouds via a polar coordinate representation and then to differentiate errors from different sources based on a randomized complete block design approach. In addition, a two-stage optimization model is proposed to find the best configuration of the multi-resolution framework. Findings The proposed framework can not only distinguish errors attributed to different sources but also evaluate the performance and consistency of alignment results under different levels of details. Practical implications A sample part with different featured layers, including a simple free-form layer, a defective layer and a layer with internal features, is used to illustrate the effectiveness and efficiency of the proposed framework. The proposed alignment method outperforms the widely used iterative closest point algorithm. Originality/value This work fills a research gap of state-of-the-art studies by automatically quantifying different types of error inherent in manufacturing, measuring and part alignment.