The ability of additive manufacturing (AM) processes to produce components with virtually any geometry presents a unique challenge in terms of quantifying the dimensional quality of the part. In this paper, a novel spectral graph theory (SGT) approach is proposed for resolving the following critical quality assurance concern in AM: how to quantify the relative deviation in dimensional integrity of complex AM components. Here, the SGT approach is demonstrated for classifying the dimensional integrity of standardized test components. The SGT-based topological invariant Fiedler number (λ2) was calculated from 3D point cloud coordinate measurements and used to quantify the dimensional integrity of test components. The Fiedler number was found to differ significantly for parts originating from different AM processes (statistical significance p-val. < 1%). By comparison, prevalent dimensional integrity assessment techniques, such as traditional statistical quantifiers (such as mean and standard deviation) and examination of specific facets/landmarks failed to capture part-to-part variations, and thus proved incapable of ranking the quality of test AM components in a consistent manner. In contrast, the SGT approach was able to consistently rank the quality of the AM components with a high degree of statistical confidence independent of sampling technique used. Consequently, from a practical standpoint, the SGT approach can be a powerful tool for assessing the dimensional integrity of AM components, and thus encourage wider adoption of AM capabilities.
Skip Nav Destination
ASME 2016 11th International Manufacturing Science and Engineering Conference
June 27–July 1, 2016
Blacksburg, Virginia, USA
Conference Sponsors:
- Manufacturing Engineering Division
ISBN:
978-0-7918-4990-3
PROCEEDINGS PAPER
Three Dimensional Point Cloud Measurement Based Dimensional Integrity Assessment for Additive Manufactured Parts Using Spectral Graph Theory Available to Purchase
Prahalad K. Rao,
Prahalad K. Rao
Binghamton University, Binghamton, NY
Search for other works by this author on:
Chad E. Duty,
Chad E. Duty
University of Tennessee, Knoxville, TN
Search for other works by this author on:
Rachel J. Smith
Rachel J. Smith
University of California, Irvine, CA
Search for other works by this author on:
Prahalad K. Rao
Binghamton University, Binghamton, NY
Zhenyu Kong
Virginia Tech, Blacksburg, VA
Chad E. Duty
University of Tennessee, Knoxville, TN
Rachel J. Smith
University of California, Irvine, CA
Paper No:
MSEC2016-8516, V002T04A048; 14 pages
Published Online:
September 27, 2016
Citation
Rao, PK, Kong, Z, Duty, CE, & Smith, RJ. "Three Dimensional Point Cloud Measurement Based Dimensional Integrity Assessment for Additive Manufactured Parts Using Spectral Graph Theory." Proceedings of the ASME 2016 11th International Manufacturing Science and Engineering Conference. Volume 2: Materials; Biomanufacturing; Properties, Applications and Systems; Sustainable Manufacturing. Blacksburg, Virginia, USA. June 27–July 1, 2016. V002T04A048. ASME. https://doi.org/10.1115/MSEC2016-8516
Download citation file:
53
Views
Related Proceedings Papers
Related Articles
Assessment of Dimensional Integrity and Spatial Defect Localization in Additive Manufacturing Using Spectral Graph Theory
J. Manuf. Sci. Eng (May,2016)
Recurrence Network-Based 3D Geometry Representation Learning for Quality Control in Additive Manufacturing of Metamaterials
J. Manuf. Sci. Eng (November,2023)
Deep Learning of Variant Geometry in Layerwise Imaging Profiles for Additive Manufacturing Quality Control
J. Manuf. Sci. Eng (November,2019)
Related Chapters
Getting Ready for Production
Total Quality Development: A Step by Step Guide to World Class Concurrent Engineering
Optimizing X-Ray Computed Tomography Settings for Dimensional Metrology Using 2D Image Analysis
Structural Integrity of Additive Manufactured Materials and Parts
Importance of Quality Control in Reducing System Risk, a Leason Learned from the Shuttle and a Recommendation for Future Launch Vehicles (PSAM-0204)
Proceedings of the Eighth International Conference on Probabilistic Safety Assessment & Management (PSAM)