Quantitative Detection of Minor Defects in Metal Materials Based on Variation Coefficient of CT Image

Abstract

In order to perform precise quantitative detection of internal minor defects in additive manufacturing of metal materials based on industrial CT, a quantitative detection method for minor defects in metal materials based on gray-scale variation coefficient of minor defects in CT image is presented. Firstly, based on the analysis of gray-scale features of minor defect regions in CT image, a minor defect image response model is constructed by combining the point spread function (PSF) and noise approximate Gaussian distribution, and the correspondence between the minor defects of different sizes and the local variation coefficient of CT image is derived, and a quantitative detection method based on the coefficient of variation of minor defect region in CT image is established for minor defects of AM metal materials. Industrial CT experiments were then conducted by processing a series of minor defect reference test blocks of different sizes with TC4 titanium. Experiment results show that the defect size and the coefficient of variation reflect a good linear relationship of monotonous increase, with correlation more than 0.996, and the quantitative results of minor defects based on the coefficient of variation method accord well with the measurement results of the microscope, with relative error controlled within 4%. Obviously, this method characterized by simple measurement process and high practical value has improved the quantitative precision of minor defects in industrial CT.