Using fuzzy sets to estimate the geometric parameters of surface damage
- 17 Downloads
This paper considers the influence of the variable parameters of a surface defect detection algorithm on the result of its operation. A method for estimating the influence of the variable parameters on the recognized geometric characteristics of the surface defect network is proposed. This method is based on representing the basic zones of the skeleton of the surface damage network in the form of compact fuzzy sets in two-dimensional space (fuzzy quasi-points). The set of points of the recognized object obtained for various combinations of the test algorithm parameters is represented in the form of a fuzzy set with a certain membership function. A method for calculating the geometric parameters of the damage network (length and slope) by means of fuzzy geometry is considered, and its use for determining the geometric parameters of the damage network of a continuous casting roller is demonstrated.
Keywordsimage recognition damage detection fuzzy quasi-point fuzzy geometry fuzzy distance
Unable to display preview. Download preview PDF.
- 2.I. Konovalenko, P. Maruschak, A. Menou, et al., “A Novel Algorithm for Damage Analysis of Fatigue Sensor by Surface Deformation Relief Parameters,” in Intern. Symp. on Operational Research and Applications (ISORAP), Marrakesh, Morocco, 2013, pp. 678–684.Google Scholar
- 5.I. V. Konovalenkoand and P. O. Marushchak, “Error Analysis of an Algorithm for Identifying Thermal Fatigue Cracks,” Avtometriya 47 (4), 49–57 (2011) [Optoelectron., Instrum. Data Process. 47 (4), 360–367 (2011)].Google Scholar
- 7.P. Maruschak, V. Gliha, I. Konovalenko, et al., “Physical Regularities in the Cracking of Nanocoatings and a Method for an Automated Determination of the Crack-Network Parameters,” Materials and Technology 46 (5), 525–529 (2012).Google Scholar
- 8.S. V. Panin, V. V. Titkov, and P. S. Lyubutin, “Efficiency of Vector Field Filtration Algorithms in Estimating Material Strain by the Method of Digital Image Correlation,” Avtometriya 49 (2), 57–67 (2013) [Optoelectron., Instrum. Data Process. 49 (2), 155–163 (2013)].Google Scholar
- 9.I. V. Konovalenko and P. O. Maruschak, “Automated Method for Studying the Deformation Behavior of a Material Damaged by a Thermal Fatigue Crack Network,” Avtometriya 49 (3), 36–43 (2013) [Optoelectron., Instrum. Data Process. 49 (3), 243–249 (2013)].Google Scholar