An Improved multivariate generalised likelihood ratio control chart for the monitoring of point clouds from 3D laser scanners.
Saved in:
| Title: | An Improved multivariate generalised likelihood ratio control chart for the monitoring of point clouds from 3D laser scanners. |
|---|---|
| Authors: | Stankus, Sue E.1 (AUTHOR), Castillo-Villar, Krystel K.1 (AUTHOR) krystel.castillo@utsa.edu |
| Source: | International Journal of Production Research. Apr2019, Vol. 57 Issue 8, p2344-2355. 12p. 1 Color Photograph, 1 Diagram, 6 Charts, 3 Graphs. |
| Subjects: | Optical scanners, Spatiotemporal processes, Quality control charts, Experimental design, Metrology, Three-dimensional printing |
| Abstract: | Statistical quality control techniques are crucial for manufacturing companies with tight tolerances but high-volume data generated from laser scanners has pushed the limits of traditional control charts. In a previous work, multivariate generalised likelihood ratio control (MGLR) chart was used to identify process shifts and locate defects on artefacts by converting 3D point cloud data to a 2D image. This paper presents a 3D MGLR control chart that retains the 3D nature of the point cloud data and uses a Fourier transform of the point errors. The average run length (ARL1) of the proposed 3D MGLR was tested using a designed experiment with ten replications and varying the number of past scans and number of Regions of Interest (ROIs). The designed experiment was repeated using three defects: incorrect surface curvature, surface scratch, and surface dent. The proposed methodology identified the dent while the prior methodology never identified it. In addition, the proposed methodology had a significantly shorter ARL1 than the prior methodology for the scratch and no significant difference in the ARL1 for the incorrect surface curvature. The proposed 3D MGLR control chart enabled the usage of 3D data without needing to convert it to a 2D image. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| Abstract: | Statistical quality control techniques are crucial for manufacturing companies with tight tolerances but high-volume data generated from laser scanners has pushed the limits of traditional control charts. In a previous work, multivariate generalised likelihood ratio control (MGLR) chart was used to identify process shifts and locate defects on artefacts by converting 3D point cloud data to a 2D image. This paper presents a 3D MGLR control chart that retains the 3D nature of the point cloud data and uses a Fourier transform of the point errors. The average run length (ARL1) of the proposed 3D MGLR was tested using a designed experiment with ten replications and varying the number of past scans and number of Regions of Interest (ROIs). The designed experiment was repeated using three defects: incorrect surface curvature, surface scratch, and surface dent. The proposed methodology identified the dent while the prior methodology never identified it. In addition, the proposed methodology had a significantly shorter ARL1 than the prior methodology for the scratch and no significant difference in the ARL1 for the incorrect surface curvature. The proposed 3D MGLR control chart enabled the usage of 3D data without needing to convert it to a 2D image. [ABSTRACT FROM AUTHOR] |
|---|---|
| ISSN: | 00207543 |
| DOI: | 10.1080/00207543.2018.1518600 |
