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Proceedings Paper

Automated real-time search and analysis algorithms for a non-contact 3D profiling system
Author(s): Mark Haynes; Chih-Hang John Wu; B. Terry Beck; Robert J. Peterman
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Paper Abstract

The purpose of this research is to develop a new means of identifying and extracting geometrical feature statistics from a non-contact precision-measurement 3D profilometer. Autonomous algorithms have been developed to search through large-scale Cartesian point clouds to identify and extract geometrical features. These algorithms are developed with the intent of providing real-time production quality control of cold-rolled steel wires. The steel wires in question are prestressing steel reinforcement wires for concrete members. The geometry of the wire is critical in the performance of the overall concrete structure. For this research a custom 3D non-contact profilometry system has been developed that utilizes laser displacement sensors for submicron resolution surface profiling. Optimizations in the control and sensory system allow for data points to be collected at up to an approximate 400,000 points per second. In order to achieve geometrical feature extraction and tolerancing with this large volume of data, the algorithms employed are optimized for parsing large data quantities. The methods used provide a unique means of maintaining high resolution data of the surface profiles while keeping algorithm running times within practical bounds for industrial application. By a combination of regional sampling, iterative search, spatial filtering, frequency filtering, spatial clustering, and template matching a robust feature identification method has been developed. These algorithms provide an autonomous means of verifying tolerances in geometrical features. The key method of identifying the features is through a combination of downhill simplex and geometrical feature templates. By performing downhill simplex through several procedural programming layers of different search and filtering techniques, very specific geometrical features can be identified within the point cloud and analyzed for proper tolerancing. Being able to perform this quality control in real time provides significant opportunities in cost savings in both equipment protection and waste minimization.

Paper Details

Date Published: 24 May 2013
PDF: 13 pages
Proc. SPIE 8791, Videometrics, Range Imaging, and Applications XII; and Automated Visual Inspection, 87911G (24 May 2013); doi: 10.1117/12.2020744
Show Author Affiliations
Mark Haynes, Kansas State Univ. (United States)
Chih-Hang John Wu, Kansas State Univ. (United States)
B. Terry Beck, Kansas State Univ. (United States)
Robert J. Peterman, Kansas State Univ. (United States)

Published in SPIE Proceedings Vol. 8791:
Videometrics, Range Imaging, and Applications XII; and Automated Visual Inspection
Fabio Remondino; Jürgen Beyerer; Fernando Puente León; Mark R. Shortis, Editor(s)

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