In the laser cladding process, there are a large number of interference factors such as arc light, metal droplets, powder splash, etc., which makes it difficult to extract the edge of the melt pool. Aiming at the problem that it is difficult to extract the edge of melt pool accurately, a method of edge detection of melt pool based on mathematical morphology and Chan-Vese active contour model is proposed in this paper. After median filtering, the binary image of the melt pool is obtained by threshold segmentation. The initial contour of the melt pool is obtained by mathematical morphology connected domain labeling algorithm. On this basis, the connected region contour is taken as the initial contour, and the Chan Vese active contour model is used to extract the edge of the melt pool. The experimental results show that this method can achieve accurate extraction of melt pool edge, and has good anti-interference performance, which provides a good foundation for subsequent melt pool feature extraction and target recognition.
Edge extraction of weld pool image is the key to realize on-line detection and control of laser cladding during the process of industrial laser manufacturing,and the accuracy of on-line control of cladding layer quality is largely determined by the accuracy of extracted weld pool edge.In this paper, a laser cladding pool image detection system based on machine vision is built to collect the molten pool image in the process of laser cladding. According to the characteristics of laser cladding pool image, the image is preprocessed. In this paper, an improved multi-structure, multi-scale and multi-directional morphological operator is proposed to process the molten pool image, and the accuracy and anti-noise ability of different edge detection methods are compared and analyzed. the improved mathematical morphology edge detection method can not only suppress noise but also extract the edge of laser cladding pool image more accurately.
A time-series prediction method for the geometric characteristics (length, length, area) of the molten pool in the laser cladding process is studied. This method uses the historical data of the change of the geometric characteristics of the molten pool to predict the geometric characteristics of the molten pool at the next moment, so as to promote the application in the control of the shape of the molten pool and the estimation of the state of the molten pool. In view of the time correlation shown by the time series of the geometric characteristics of the molten pool, a time series prediction method for the geometric characteristics of the molten pool was developed based on the Autoregressive Integrated Moving Average (ARIMA) model, and the dynamic stability index of the molten pool was proposed. The results show that the area of the molten pool has the best prediction accuracy, and the Mean Absolute Percentage Error (MAPE) is only 3.105, while the predicted MAPE of the width of the molten pool is 3.464 and the length of the predicted MAPE is 4.048. The dynamic stability index proposed can reflect the fluctuation of the molten pool.
Aiming at the images of relevant monitored objects in the process of laser cladding, a super resolution algorithm technology was proposed to optimize and enhance the key details of the images, and the enhanced image content was segmented, extracted and counted. First, construct a training of a sub-resolution convolutional neural network (SRCNN) model; the original low resolution is predicted by the weight after training, the image quality evaluation results: peak signal-to-noise ratio (PSNR) is 30.198212, structural similarity (SSIM) is 0.969966; the most based on the maximum entropy dual threshold split algorithm combined with image processing, extracting and statistics on the powder object in the segmentation result image, the number of effective powders and proportion of the original wandering map and the predicted output delay image is [112, 33.6%] and [240, 40.6%]. The research results show that the cladding image output from the original image after the super resolution model has been significantly improved in terms of clarity and quality as well as the optimization and enhancement of the details of the monitored object.
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