Abstract
This paper is conducted to explore a new characterization method as a supplement to the traditional roughness characterization. The main research includes the extraction and evaluation of damage features of ceramic surface morphology by applying wavelet methods, the extraction of damage features in surface contours by using wavelet analysis, and the quantitative evaluation of damage degree by using damage rate and damage mean spacing. By comparing various fractal dimension calculation methods, a fractal dimension method suitable for calculating the ceramic surface was selected, and the fractal method was used to describe the ceramic surface topography as a whole. By comparing different methods of calculating the fractal dimension and further verifying them with the measured three-dimensional morphology, it is found that the vibrational method is more suitable for calculating the fractal dimension of ceramic surface, and its calculation accuracy is investigated, and the results show that the method is a reliable one. Based on the fractal theory, a mathematical model of surface wear and surface sealing was established. Further study of the model shows that the surface with a large fractal dimension has a good sealing effect; the surface corresponding to the best fractal dimension is the most resistant to wear. The fractal method can characterize the complexity of the surface profile as a whole. The wavelet method can describe the ceramic surface profile from a local perspective, and the combination of the two methods can characterize the ceramic surface well. Finally, the experimental device of the ceramic surface defect detection system is constructed, and the joint debugging of hardware and software is completed. Under different light source intensities, ceramic image samples are collected, and the accuracy detection experiments of sample defective edges are conducted, and the results show that the light source has a small impact on the accuracy of ceramic defective edge detection. The results show that the light source has more influence on the accuracy of scratch detection. The results show that the system constructed in this thesis has good applicability for different ceramic sample detection.
Highlights
Due to the hard and brittle nature of ceramics, their poor thermal conductivity and wear resistance make them very difficult to machine
A large number of training samples are required for modeling using support vector techniques, and the field data collected by automatic detection equipment from the production process contains a large amount of noise, which will reduce the accuracy of the model ceramic surface image texture feature analysis when applied directly to the constructed ceramic surface image texture feature model
The entire measurement and evaluation process can be summarized as follows: (i) prepare ceramic samples; (ii) measure the surface of these samples with a Talysurf-i120 profiler and export the raw profile data using Talymap Gold, a postprocessing software that comes with Talysurf; (iii) import the exported data into the computer for data analysis and processing, and derive the evaluation parameters; (iv) evaluate the surface profile based on the derived evaluation parameters
Summary
Due to the hard and brittle nature of ceramics, their poor thermal conductivity and wear resistance make them very difficult to machine. A large number of training samples are required for modeling using support vector techniques, and the field data collected by automatic detection equipment from the production process contains a large amount of noise, which will reduce the accuracy of the model ceramic surface image texture feature analysis when applied directly to the constructed ceramic surface image texture feature model. The third chapter of this paper is the research on the texture feature analysis of ceramic surface images based on wavelet analysis, which mainly includes the selection of visual components, the analysis of interference noise in image crack detection, the research of filtering algorithm, and image sampling method.
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