An image analysis-based two-stage process parameters tuning and Surface Roughness (SR) estimation algorithm is proposed for the laser cleaning application. A Cartesian coordinate robot is utilized to collect image and implement cleaning. Before cleaning, in order to tune the proper laser parameters, first, the environment lighting is controlled for the metal image collection. Second, lots of classification features are computed for the images above. The Gray-Level Co-occurrence Matrix (GLCM) texture features, the concavo-convex region features, the histogram symmetry difference feature, and the imaging thermophysical property features are computed. Third, the initial laser parameters are created randomly and an iteration computation is performed: a Support Vector Machine (SVM) is used to forecast the cleaning effect; its inputs include the classification features and the initial laser parameters; its output is the cleaning effect degree. If the SVM output cannot fulfill user's demand, the laser parameters will be updated randomly. This iteration will be implemented constantly until the SVM output becomes valid. Then the laser cleaning will be performed. When estimating SR for the cleaned metal, multiple image features are calculated for the images after cleaning. The features include the Tamura coarseness, some GLCM features, and the convex region feature. To improve the prediction precision, different feature combinations are used for different cleaning effects. The linear function and the 3-order polynomial function are considered for the SR estimation. After tests, the accuracies of SVM, the SR prediction function, and the integrated SR control and estimation algorithm can be 90.0%, 80.0% and 80.0% approximately.

Abstract. With the rapid development of satellite astronomy technique, In order to ensure the effective operation of the spacecraft in orbit, the spacecraft requires full ground physical simulation tests. So the construction of the ground simulation test system is an important guarantee for the development of space technology [1]. In the system of full physical simulation test for lunar orbit rendezvous and docking, the large supportive stage with super smooth surface is one of the most important large-scale precision test equipment, this stage can provide a high precision horizontal support surface in the range of 40m*30m for the test load, Each test load is usually supported by three circular air cushion, whose diameter is 200mm~400mm. High pressure gas is stored in the load equipment, and is released into the air through the air holes on the air cushion, A thin layer of air film is formed between the air cushion and the stage, the thickness of gas film is 10μm~15μm, The load equipment can freely move on the surface of a large stage with the gas film. There are strict requirements for the deformation of the system, Since the floating height is 10 μm and the test load is 3T. So the altitude difference of neighboring platforms is in range of 10μm. In order to guarantee the experiment effects, the paper analyses mechanical properties of the stage, distribute index of 10μm, and provide theoretical support for subsystem [2], [3].

Shape from shading (SFS) is one of the critical techniques in 3D shape recovery in computer vision. The task of SFS is to reconstruct 3D shape of the visible surface of an object from one single picture using its gray variation. Analyzing the 3D shape of welding pool is important to evaluate the welding quality. During the disk laser welding experiments, a high-power laser was used as the auxiliary illuminant, and a high-speed image acquisition system with infrared filter was applied to capture the welding pools in real time. The slant and tilt of illuminant source were estimated by statistics to reconstruct the 3D shape of welding pool visible surface by using the localization method of SFS. Methods of median filter and cubic spline interpolation were used to denoise images and smooth the image shapes. Experimental results showed that the proposed technique could reconstruct parts of welding pool effectively.

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