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The left and right-turning scheme of weldments was adopted to detect small porosities in laser weldments with complex structures. Detection images were obtained in this study. An algorithm that determines the optimal structure elements for morphology simulation was provided to achieve fidelity segmentation of small porosities. Mathematical models for the defect depth and deviation of bulk porosities were established based on the geometric features of laser weldments. Thus, the features of spatial distribution of small porosities were determined. To improve detection efficiency, the automatic extraction of spatial position data for bulk porosities was divided into two parts: automatic extraction of projection distance, which is important for the determination of weld porosity depth and deviation, and automatic extraction of porosity size. The porosity distribution in the longitudinal direction was obtained by automatic searching, and the features of the spatial distribution of weld porosities were procured. A 3D model of laser weldments was established to visualize the spatial distribution of porosities. Extracted data were inputted to the model, and the spatial positions of bulk porosities were visualized.

In order to study the segmentation and quantitative problem of small defects in precision weldments with complex structure, an automated image processing system was setup based on the Visual Basic 6.0 development environment. The weld zones were extracted by adopting the method of twice automatic threshold, and the small defects were segmented successfully by using sharpen, smoothing processing and background subtraction in the extracted weld zones. To determine the spatial distribution features of small defects, calculation formula of defects depth and deviation were deduced individually, and the projection distance of small defects can be extracted automatically also. The image processing system can achieve the goal of small defects segmentation and automatic extraction of projection distance. The depth and deviation of small defects can be obtained through the above deduced formula, and the longitudinal distribution of small defects can be obtained from the detection image, then the spatial distribution features of small defects can be determined.