Image-based Material Components,surface Micro Crack-defect Characterization And Its Applications

Material characterization techniques are used to obtain the information of materials structure,components or other information,which plays an important role in the research.Microscope technology is an important method of material microstructure characterization,in which images are often used as the characterization of materials.Hence,it is scientific and reasonable to apply image processing techniques to analyze the material microstructure information.Currently,some applications can be found in this research area.However,there are two key problems need to be solved: the first one is how to deeply understand the influence of material properties with the material characteristics,and the second one is how to handle the large amount of material image data.For these problems,we take copper coated graphite and polyester thermoplastic ester elastomer(TPEE)as the research materials in this study,and investigate the algorithms of copper particle detection and crack-defect location based on the material characterization with digital image processing.In order to understand the properties of these materials,the uniformity of copper particles and prediction of TPEE tensile properties is studied in this research.The main work and innovations are as follows:(1)Analyze the relationship between material components and crack-defect.Then,image preprocessing methods,including image enhancement and denoising,the method based on local is presented.Experiments are conducted with material images,and the results imply that image preprocessing is necessary.(2)Copper graphite is used as the research object.According to the low intensity of TEM image and the local separability of copper particles in the image,the copper particle detection method based on the local iterative threshold and the global threshold is proposed.Through morphology processing and area filtering,copper particles are accurately detected.Experimental results indicate the effectiveness of the proposed method.The information of copper particles is thoroughly excavated.A uniformity quantification method based on global Shannon Entropy and local relative distance distribution is proposed.Experimental results show that the proposed method is not only consistent with the statistical analysis and human observations,but also can ensure the relative uniformity of copper particles in different TEM images,which mean the proposed method is effective and reasonable.(3)Polyester thermoplastic ester elastomer are used as our research objects.According to image reflection and unclear crack-defect,TPEE image reconstruction method based on the maximum gradient of different orientations is proposed.Experimental results demonstrate that the proposed method is not only highlight the feature of crack-defect,but also reduce the noise.A novel crack-defect detection algorithm is proposed,which is based on the change of circular degree of local connected region.Experimental results show that the proposed method not only can detect the crack-defect in different TPEE images,but also has a robustness of subimage number and noise,which demonstrate the effectiveness and reasonable of the proposed method.(4)Apply the information of crack-defect of TPEE material to predict its tensile properties.The prediction method based on image processing and Restricted Boltzmann Machine(RBM)is proposed.The prediction results of the proposed method are consistent with the results of the tensile test,which verifies the effectiveness and reasonableness of the proposed method.The influence of parameters on the prediction results is analyzed,and the proposed model is compared with the neural network prediction model.