Technology Of Metallographical Image Processing Based On Mathematical Morphology And Fractal

With the rapid development of production and the great progress of science and technology, the reqirements for reliability and safety of all kinds of metal materials applied in machinery and equipment have become more sophisticated, which requires more sophisticated methods to inspect and control the quality of the metal materials. So far, the metallographical technology has been the most easy and effective method of research and testing that widely applied in materials science and engineering. The computer-aided quantitative metallographical analysis, with a high accuracy, speed, etc., now is becoming the core of the metallographical technology, while the technology of the metallographical image processing is the key to the computer-aided quantitative metallographical analysis. Therefore, the research of the key technologies in the metallographical image processing has a great practical value in the developments and applications of the metallographical technology.The key technology in the metallographical image processing includes edge detection, segmentation (including the restoration and reconstruction of grain edges) and fractal dimension calculation of the metallographical image. Mathematical morphology is a very practical method of metallographical image processing. The key of the method is the construction of two basic operations namely dilation and erosion, and the construction of structural elements in the mathematical morphology. However, the traditional morphological dilation affects the continuity and uniformity of gray of the metallographical image. In addition, the form and size of the structural elements in the traditional operations do not change, which means that the traditional operations are uniformily balanced processings to the whole morphological image and would cause the image to over-processing (over-segmentation) or less-processing (less-segmentation) that reduces the accuracy of the computer-aided quantitative metallographical analysis. Therefore, in this paper, the key processes in the metallographical image processing——edge detection and image segmentation (including the restoration and reconstruction of grain edges) are researched first based on mathematical morphology theory. Some theories and methods relevant to the edge detection and image segmentation are suggested and verified theoretically and experimentally. Fractal theory is a very active branch of modern mathematics and nonlinear science.Fractal dimension, now widely applied in image processing and analysis, is the most important aspect of the application of fractal theory. There are several kinds of mathematical definition of fractal dimension, but how to calculate the fractal dimension of the metallographical image is a pressing issue for further study. Therefore, in this paper, the characteristics of the metallographical image and the algorithm of the fractal dimension are analyzed thoroughly. Some theories and methods are put forward combined with the characteristics and verified by experiments.The main results of research and innovation in this paper include:(1) For the false detection, leak detection, and multi-pixel width edge in the existing metallographical image edge detection, the "error processing" problem, and for the complexity of metallographical images that contains a large number of impurities and noise, edge detection algorithms based on multi-scale and muti-form of morphological structureal elements are proposed in this paper. A large number of experiments show that this algorithm is more effective and the edges detected by it are more accurate, continous and smooth in comparison to the traditional edge detection methods. This provides a technical support for the accurate measurements of the grain parameters and calculation of the fractal dimension in the metallographical images.(2) The adverse effects of traditional operation of dilation on the gray continuity in the metallographical image is researched theoretically, which is related to the size of structural element. The traditional definition of dilation is modified reasonablly which lays the theoretical foundation for the multi-scale geodesic deliltion, the technology of restoration and reconstruction of the grain edges in the metallographical image. In addition, demonstrate theoretically that the traditional operation of erosion maintain the continuity and uniformity of gray in the metallographical image, which provides the theory basis for the selection of scale of structure elements.(3) Based on the conclusions proved in (2) and related to the effects of traditional operation of dilation and erosion on the gray continuity in the metallographical image, aimed at the shortage of the traditional watershed segmentation algorithm, and combined with the features of the metallographical image, a new algorithm based on the modified delition, called multi-scale geodesic deliltion, the technology of restoration and reconstruction of the grain edges in the metallographical image, is proposed in this paper. In this algorithm, the traditional delition is replaced by the modified delition, which improves the accuracy and clarity of the grain edges restored and reconstructed and reduces greatly the phenomenon of“over-segmentation”,“less-segmentation”or“wrong- segmentation”in the metallographical image. In addition, the traditional iterative erosion with single-scale is replaced by the multi-scale iterative erosion, and the traditional repeat geodesic delition with single-scale is replaced by the multi-scale geodesic delition, which reduces the program run time greatly and improves the efficiency of the restoration and reconstruction of grain edges in the metallographical image.(4) Combined with the characteristics of the metallographical image, using the edge detection algorithms based on multi-scale and muti-form of morphological structureal elements, an algorithm of fractal dimension in the metallographical image based on the morphological dilation body coverage is suggested in this paper. It is proved by a large number of experiments that the algorithm is more effective compared with the current algorithm and has certain advantages over the current algorithm.