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Intelligent automation aiding rapid surface feature quantification in 3D

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conference contribution
posted on 14.08.2014 by Mitul Tailor, Jon Petzing, Michael Jackson, Robert M. Parkin
Automatic small surface feature inspection of high-precision components requires superior measurement and automated analysis systems. Early identification and quantification (depth, area and volume) is a key aspect in quality assurance in order to check the health of the manufactured part. Human visual analysis of surface feature inspection is qualitative, subjective and time consuming. Three-dimensional (3D) robotic inspection should provide a more robust and systematic quantitative approach for surface defect measurement. 3D measuring instruments typically generate point cloud data as an output, although via different principles. Data processing of point cloud data is often subject to repeatability issues causing significant concern with data confidence. This research is concerned with the measurement of novel traceable sub-millimetric surface defects and the development of a novel, robust, repeatable and mathematical solution for automatic defect detection and quantification. This is then extended to a surface defect on a plain bearing measured in real-time using a 3D measuring instrument mounted on a 6-axis robot and quantified using the novel algorithm. The results show that the new surface defect measurement and quantification is more robust, efficient, and repeatable than existing solutions.

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

The 14th Mechatronics Forum International Conference Proceedings of the 14th Mechatronics Forum International Conference Mechatronics 2014

Volume

1

Issue

1

Pages

197 - 201 (5)

Citation

TAILOR, M. ... et al, 2014. Intelligent automation aiding rapid surface feature quantification in 3D. IN: de Vin, L. and Solis, J. (eds). Proceedings of the 14th Mechatronics Forum International Conference, Mechatronics 2014, 16th-18th June 2014, Karlstad, Sweden. Karlstad Universitet, pp. 197 - 201.

Publisher

Karlstad Universitet

Version

AM (Accepted Manuscript)

Publication date

2014

Notes

This is a conference paper.

ISBN

9789170635649

Language

en

Location

Karlstad, Sweden

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