In order to predict the laser cleaning quality of composite paint layer and realize the controllable paint removal of composite paint layer, this paper proposes a machine-learning based laser cleaning quality prediction method for composite paint layer. The aluminum alloy substrate surface is uniformly coated with 20 μm green epoxy primer and 43μmwhite polyurethane topcoat as experimental samples for laser cleaning experiments; based on the experimental data combined with the three machine learning algorithms (support vector machine SVR, BP neural network, and random forest RF) to establish a prediction model between the process parameters and the cleaning quality. The experimental results show that, compared with the RF model and BP neural network model, the SVR model is more accurate in predicting the quality of laser cleaning of composite paint layers, and the coefficient of determination of the prediction model is 0.961, the root mean square error is 1.738, and the average absolute error is 1.5162.This study obtains the prediction model of the quality of removing the paint thickness with high accuracy, realizes the effective prediction of the quality of the laser removing the paint for the It lays a model foundation for further research on intelligent control of laser cleaning of composite paint layers.
KEYWORDS: Control systems, Laser applications, Semiconductor lasers, Laser stabilization, Beam controllers, Laser development, Control systems design, Laser systems engineering, Algorithm development, Process control
Laser cleaning technology as a green and environmentally friendly cleaning method, in recent years in the field of industrial cleaning rapid development. This paper introduces the current status of research on the main parts of the laser cleaning machine control system, including laser cleaning detection system, scanning vibrator control system, laser power control system, control algorithms and other control parts. The characteristics of existing control systems and control methods are reviewed, and it is found that the main problems in this field are the difficulty and high cost of control system development, few links between different control methods and low automation, etc. An outlook on future development is given, including optimization of laser cleaning machine control research directions, enhancement of control technology and establishment of cleaning databases.
In order to achieve the camera calibration, the calculation process of the camera’s internal and external parameters was obtained through the established camera calibration model. Based on the coplanar points, the camera calibration model was simplified. With distortion model and Levenberg-Marquardt algorithm, the system calibration’s accuracy was improved. The experimental results showed that the calibration error was smaller and the error data was more concentrated, which realized the accurate calibration of the camera.
We have developed a kind of compact diode-pumped solid-state (DPSS) lasers targeting laser cleaning applications in railway, automobile and aeroplane industries. Based on a master oscillate power amplifier (MOPA) configuration, the laser output is coupled to optical fibers with core diameters of 0.2-0.4 mm depending on the output power. The master oscillator is an actively Q-switched Nd:YAG laser in a thermal insensitive cavity for good beam quality at high average power. The laser repetition rate is adjustable in 1-10 kHz. At a typical 8 kHz, we obtained pulse energy ~50 mJ, pulse width ~50 ns, peak power ~0.8 MW and M2~22, which can satisfy the requirements of most industrial laser cleaning and take advantage over commercial fiber lasers with similar output powers in terms of processing efficiency and machineable materials. The increase of either pump power or repetition rate can elevate average laser output power up to 1 kW.
During the process of collecting laser bath temperature data by CCD industrial camera, due to the influence of laser cladding process and installation location of mechanical equipment, the lens axis of CCD industrial camera is not perpendicular to the surface of the molten pool, resulting in positioning error caused by tilting photography. In order to eliminate the influence of positioning error and restore the true shape of the molten pool, a position calibration method for the temperature image of molten pool based on image processing technology was proposed. The experimental results show that after image processing the acquired image data of the molten pool was restored image morphology, which laid the foundation for subsequent image processing.
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