Automatic Process Parameters Tuning and Surface Roughness Estimation for Laser Cleaning

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.

Reflection Coefficient Estimation of Femtosecond Laser Surface Processing Using Support Vector Regression

“…Third, the product quality data package can be formed according to RC prediction results to judge whether the products are qualified or unqualified. To improve the automation level of system, both the femtosecond laser and SEM can be installed into an integrated device (see [12]). The proposed method at least has three merits.…”

“…In this paper, a blackening effect estimation, i.e., the RC estimation of femtosecond laser surface processing is proposed. First, the femtosecond laser processing is performed and the laser process parameters [12] are saved. Second, after laser processing, the ablation diameters of femtosecond laser are measured by typical SEM images.…”

Imaging Feature Analysis-Based Intelligent Laser Cleaning Using Metal Color Difference and Dynamic Weight Dispatch Corrosion Texture

“…In [12], a coupling algorithm of dynamic threshold location was proposed to solve the problem of uneven illumination on the material surface and separate the qualified area from the unqualified area. In [13], a method of surface roughness estimation based on image analysis was proposed. The authors used the machine vision method to judge the effect of laser cleaning and determine whether the cleaning performance was qualified.…”