Metrology and uncertainty propagation

Improving the Pipeline of an Optical Metrology System 

ABSTRACT

This work is a study of the typical vision system pipeline, in the different phases, necessary to achieve optimal inspection in an industrial operation. The first step is the study of the light alignment to monitor and achieve an optimal light alignment system, in order to eliminate the effects of misalignment. The algorithm was tested with a not-optimal system to ascertain its efficiency and effectiveness. In the second phase, a deep study of the calibration process is carried out to address the effect of different parameters as the camera focus among others. Endocentric and telecentric lenses are used in the image acquisition and a comparative analysis is obtained using a multivariable statistical analysis to study the influence of each parameter in the calibration process: camera focus, exposure time, calibration plate tilt and number of images used. In the third proposal, an object alignment algorithm is developed to address the challenge of object alignment during a measurement process. Object plane alignment is key point for achieving good repeatability of object measurements in all orientations. A complete study of the impact of every single pipeline phase is carried out. Besides, a deep uncertainties analysis is perfermed in a real application of gears dimensional control.

CONTENT

This paper aims to analyze the effect of four calibration parameters: camera focus, exposure time, calibration plate tilt and number of images, on the calibration accuracy. Endocentric and telecentric lenses are used in the image acquisition and a comparative quality analysis of the calibration result is obtained using statistical methods. A sample of 2176 images is used to generate the population and the calibration error is obtained for the different values of the parameters of interest. To study the influence of each parameter in the calibration error, a multivariable statistical analysis is performed. Statistically significant results were obtained for all parameters, except in the exposure time parameter, leading to the conclusion that the calibration results (and hence the measurement accuracy) can be improved by choosing the appropriate calibration parameters.

We have developed 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.

PAPERS

Journals:

• • Moru, D.K., and Borro, D., “A machine vision algorithm for quality control inspection of gears”, The International Journal of Advanced Manufacturing Technology, Vol. 106, No. 1-2, pp. 105-123. January 2020. (pdf).

  • Moru, D.K., and Borro, D., “Analysis of different parameters of influence in industrial cameras calibration processes”, Measurement, Vol. 171, 108750. February 2021. (pdf)

  • Moru, D.K., and Borro, D., “Improving optical pipeline through better alignment and calibration process”, The International Journal of Advanced Manufacturing Technology, Vol. 114, No. 3-4, pp. 797-809. May, 2021. (pdf).

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