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A machine vision algorithm for quality control inspection of gears

  • Desmond K. MoruEmail author
  • Diego Borro
ORIGINAL ARTICLE
  • 79 Downloads

Abstract

Quality control has become a priority in the inspection processes of industrial manufacturing of gears. Due to the advancement of technology and the realizations of Industry 4.0, smart factories demand high precision and accuracy in the measurements and inspection of industrial gears. Machine vision technology provides image-based inspection and analysis for such demanding applications. With the use of software, sensors, cameras, and robot guidance, such integrated systems can be realized. The aim of this paper is to deploy an improved machine vision application to determine the precise measurement of industrial gears, at subpixel level, with the potential to improve quality control, reduce downtime, and optimize the inspection process. A machine vision application (Vision2D) has been developed to acquire and analyze captured images to implement the process of measurement and inspection. Firstly, a very minimum calibration error of 0.06 pixel was obtained after calibration. The calibrated vision system was verified by measuring a ground-truth sample gear in a Coordinate Measuring Machine (CMM), using the parameter generated as the nominal value of the outer diameter. A methodical study of the global uncertainty associated with the process is carried out in order to know better the admissible zone for accepting gears. After that, the proposed system analyzed twelve other samples with a nominal tolerance threshold of ± 0.020 mm. Amongst the gears inspected, the Vision2D application identified eight gears which are accepted and four bad gears which are rejected. The inspection result demonstrates an improvement in the algorithm of the Vision2D system application when compared with the previous existing algorithms.

Keywords

Quality control Machine vision Inspection metrology Accuracy Precision Image-based process 

Notes

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.CeitDonostia/San SebastiánSpain
  2. 2.University of Navarra, TecnunDonostia/San SebastiánSpain

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