Aerospace Panels Fixtureless Inspection Methods with Restraining Force Requirements; A Technology Review

Abenhaim, Gad N.; Tahan, Antoine; Desrochers, Alain; Lalonde, Jean‐François

doi:10.4271/2013-01-2172

Cited by 6 publications

(5 citation statements)

References 11 publications

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“…Typical fuselage structural components include beams, ribs, frames, aircraft panels, etc. There are two common types of measurement: online measurements using CNC machine tools [12] and offline measurements using flexible tooling [13]. The part features involved are generally flat, slender, multi-cavity, and weakly rigid.…”

Section: Introductionmentioning

confidence: 99%

Feature extraction from point clouds for rigid aircraft part inspection using an improved Harris algorithm

Li¹,

Huang²,

Li³

et al. 2018

Meas. Sci. Technol.

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In the process of feature extraction, geometric information about feature points is based on the nearest 1-ring field points, which are usually the edge points of the face adjacent to the feature line. The high structural complexity of rigid aircraft components leads to recognition of false feature points. Therefore, this paper proposes an improved Harris algorithm designed to improve the accuracy of feature extraction. It combines the discrete curvature and the normal vector in a way that is suitable for use with point clouds. Key to this approach is the use of changes in the gradient rather than changes in the curvature to identify corner, plane, and crease points. Our method accurately eliminates pseudo-feature points that are too close to actual feature points in order to refine potential feature points. We demonstrate the efficiency and robustness of the proposed method by validating it against standard models and actual scan data and by comparing it with other feature detection algorithms.

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Section: Introductionmentioning

confidence: 99%

Feature extraction from point clouds for rigid aircraft part inspection using an improved Harris algorithm

Li¹,

Huang²,

Li³

et al. 2018

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…Figure 1-b illustrates an example of such an inspection fixture for the part shown in a free-state in Figure 1-a. [2] Recent improvements in data acquisition devices, such as three-dimensional (3D) scanners, and in computational calculations, allow an ongoing progress towards Computer-Aided Inspection (CAI) methods. These methods facilitate inspection by using a comparison between the scan model of a manufactured part and its Computer-Aided Design (CAD) model.…”

Section: Introductionmentioning

confidence: 99%

“…Figure 1: A regular aerospace panel, a) in free-state, b) constrained by fixing jigs on the inspection fixture[2] …”

mentioning

confidence: 99%

Assessment of the Robustness of a Fixtureless Inspection Method for Nonrigid Parts Based on a Verification and Validation Approach

Karganroudi

Cuillière

Vincent

et al. 2017

Journal of Verification, Validation and Uncertainty Quantification

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The increasing practical use of computer-aided inspection (CAI) methods requires assessment of their robustness in different contexts. This can be done by quantitatively comparing estimated CAI results with actual measurements. The objective is comparing the magnitude and dimensions of defects as estimated by CAI with those of the nominal defects. This assessment is referred to as setting up a validation metric. In this work, a new validation metric is proposed in the case of a fixtureless inspection method for nonrigid parts. It is based on using a nonparametric statistical hypothesis test, namely the Kolmogorov–Smirnov (K–S) test. This metric is applied to an automatic fixtureless CAI method for nonrigid parts developed by our team. This fixtureless CAI method is based on calculating and filtering sample points that are used in a finite element nonrigid registration (FENR). Robustness of our CAI method is validated for the assessment of maximum amplitude, area, and distance distribution of defects. Typical parts from the aerospace industry are used for this validation and various levels of synthetic measurement noise are added to the scanned point cloud of these parts to assess the effect of noise on inspection results.

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“…In Figure 1-b, the part introduced in Figure 1-a in a free-state is shown as constrained on such a fixture set. [4] Beholden to the improvements in computer graphics and optic scanners, manual and tactile methods of measurement and inspection have progressively been replaced by Computer Aided Inspection (CAI) methods. CAI methods are noteworthy due to the ability of automating all the inspection process which speeds up and increases the accuracy of inspection by eliminating human intervention and its inseparable error.…”

Section: Introductionmentioning

confidence: 99%

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

Karganroudi

Cuillière

Vincent

et al. 2016

Int J Adv Manuf Technol

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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, 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.

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Aerospace Panels Fixtureless Inspection Methods with Restraining Force Requirements; A Technology Review

Cited by 6 publications

References 11 publications

Feature extraction from point clouds for rigid aircraft part inspection using an improved Harris algorithm

Feature extraction from point clouds for rigid aircraft part inspection using an improved Harris algorithm

Assessment of the Robustness of a Fixtureless Inspection Method for Nonrigid Parts Based on a Verification and Validation Approach

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

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