Abstract
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.
Highlights
The laser cleaning can remove the reminders in the surface of workpiece by the principles [1] of mechanic resonance, thermal expansion, or evaporation and gasification processes, etc
APPLICATION DEFINITION AND ITS PROPOSED HARDWARE SOLUTION This paper focuses on the laser cleaning application of a kind of marine carbon steel which works in a stable nature state
It can be concluded that: comparing with the image data in Fig. 8 (d) which are covered by the corrosion layers, it is possible to use the classification degree of laser cleaning effect and the corresponding image features of the cleaned metal to predict the Surface Roughness (SR) precisely
Summary
The laser cleaning can remove the reminders in the surface of workpiece by the principles [1] of mechanic resonance, thermal expansion, or evaporation and gasification processes, etc. An iteration computation is carried out to find the proper laser parameters When carrying out the latter estimation, different image features are used for the images with different cleaning effects. The main effect result of external environment on its surface is the metal corrosion problem [21] Another assumption is that the material stability [22] of carbon steel is comparable high after it leaves its manufacturing factory. After the image capture, a series of imaging features will be computed and the laser process parameters will be created intelligently Fourth, this system will take the output port of laser to carry out the cleaning task. Where nx and ny are the amounts of sample point in the user defined x and y coordinate axes in workpiece, respectively; η(xi, yj) represents the centered height in position (xi, yj), where the mean height calculated on the definition area has been already subtracted from that height
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