Evaluation of image based Abbott-Firestone curve parameters using machine vision for the characterization of cylinder liner surface topography

K. Deepak Lawrence, Rajalingappaa Shanmugamani, B. Ramamoorthy

Research output: Contribution to journalArticle

13 Citations (Scopus)


In this paper, a method is proposed for the evaluation of image based Abbott-Firestone curve parameters aiming to characterize the cylinder bore surface topography using machine vision. Plateau honing experiments are performed to generate sixteen cylinder liners with different surface topographies and the 2-D and 3-D Abbott-Firestone parameters are measured using a stylus instrument and Coherence Scanning Interferometer (CSI), respectively. The images are captured from the corresponding portions of the cylinder liner surfaces using a Charge Coupled Device (CCD) camera connected with different microscopic attachments. The captured images are filtered using a Butterworth high pass filter followed by the adaptation of the double step Gaussian filtering procedure specified by the ISO 13565-1. An Abbott-Firestone curve is constructed by finding the cumulative of the intensity histogram of the filtered images. Five image based parameters are evaluated from the constructed Abbott curve by adapting the procedures presented in ISO 13565-2. The computed image based Abbott-Firestone curve parameters are observed to bear a statistically significant correlation with the measured 2-D and 3-D Abbott-Firestone curve parameters. An artificial neural network (ANN) is trained and tested to arrive at the actual values of the Abbott-Firestone curve parameters using the computed image based feature parameters. The results indicate that the multiple surface topography parameters of the cylinder bore surface could be estimated/predicted with a reasonable accuracy using machine vision technique coupled with ANN.

Original languageEnglish
Pages (from-to)318-334
Number of pages17
JournalMeasurement: Journal of the International Measurement Confederation
Publication statusPublished - 2014


All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Education
  • Condensed Matter Physics
  • Applied Mathematics

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