Guangzhou Panyu Gofront Dyeing & Finishing Machinery Manufacturer Ltd

Guangzhou, China

Guangzhou Panyu Gofront Dyeing & Finishing Machinery Manufacturer Ltd

Guangzhou, China
SEARCH FILTERS
Time filter
Source Type

Gao X.,Guangdong University of Technology | Liu Y.,Guangdong University of Technology | Xiao Z.,Guangzhou Panyu Gofront Dyeing & Finishing Machinery Manufacturer Ltd. | Chen X.,Guangzhou Panyu Gofront Dyeing & Finishing Machinery Manufacturer Ltd.
Hanjie Xuebao/Transactions of the China Welding Institution | Year: 2015

A multi-sensor information fusion method based on support vector machine was studied to analyze the high-power disk laser welding status. During high-power disk laser welding, the metallic plume, spatters and molten pool are important phenomena which are related to the welding quality. An ultraviolet and visible sensitive video camera was used to capture the metallic plume and spatter dynamic images, and another infrared sensitive video camera was used to capture the molten pool images. The image processing and pattern recognition technologies were applied to extract the welding characteristics information and analyze the principal components. Weld bead width was used as a characteristic parameter that reflects the welding stability. After data normalization and characteristic analysis, the multi-sensor information was fused by the support vector machine, and the grid search method and particle swarm optimization were used to optimize the experimental parameters of support vector machine. Finally a fusion model based on support vector machine was established to estimate the weld bead width. Experimental results showed that the multi-sensor information fusion based on support vector machine could effectively predict the weld bead width, thus providing an experimental evidence for monitoring the high-power disk laser welding status. © 2015, Harbin Research Institute of Welding. All right reserved.


Gao X.,Guangdong University of Technology | Mo L.,Guangdong University of Technology | Xiao Z.,Guangzhou Panyu Gofront Dyeing & Finishing Machinery Manufacturer Ltd | Chen X.,Guangzhou Panyu Gofront Dyeing & Finishing Machinery Manufacturer Ltd | Katayama S.,Osaka University
International Journal of Advanced Manufacturing Technology | Year: 2015

Seam tracking ability of a welding system is significant for welding process and obtaining good welds. It is necessary to realize weld seam detection and tracking for welding automation. Kalman filter (KF) is applied to get the optimal state estimation of micro-gap (whose width is less than 0.2 mm) butt joint weld position. A magneto-optical sensor was used to obtain the weld information. The weld position was detected by the maximum entropy segmentation method, and the weld position parameter from a magneto-optical image was extracted as a state eigenvector, which included the weld position at previous sampling time and the variation of weld position. The state equation based on the weld position parameter and the measurement equation for the weld position are established. Considering that the system dynamic noises were white noises, a traditional Kalman filtering algorithm was developed with white noises, and the optimal state estimation of the weld position was obtained. The influence of noise statistical uncertainty characteristics on Kalman filtering was analyzed. Experimental results show that the Kalman filter algorithm can effectively restrain the noise jamming, and the effect of Kalman filter is affected by noise statistical characteristics directly. © 2015 Springer-Verlag London


Mo L.,Guangdong University of Technology | Gao X.,Guangdong University of Technology | Xiao Z.,Guangzhou Panyu Gofront Dyeing & Finishing Machinery Manufacturer Ltd. | Chen X.,Guangzhou Panyu Gofront Dyeing & Finishing Machinery Manufacturer Ltd.
Hanjie Xuebao/Transactions of the China Welding Institution | Year: 2016

This paper proposes a novel method to realize accurate seam tracking of butt micro-gap (less than 0.1mm) joint during laser welding based on the magneto-optical color images. Images of weld seam zone are captured by a magneto-optical sensor, and grayscale distribution of the weld seam magneto-optical images in the RGB(Red, Green, Blue)and HSV(Hue, Saturation, Value) color space are analyzed. The grayscale map of component of the RGB figure is extracted, and weld seam edge is determined based on the threshold derived from the grayscale distribution curve of each component. Also, the outline of weld seam transitional zone is obtained by integrating weld seam edges of the three components. The histogram of each HSV component is analyzed to determine the threshold and then the synthetic seam transition zone segmentation is obtained. Experimental results show that the proposed method can effectively detect micro-gap weld seam which is generally hard to distinguish by human eyes. © 2016, Harbin Research Institute of Welding. All right reserved.


Gao X.,Guangdong University of Technology | Mo L.,Guangdong University of Technology | Xiao Z.,Guangzhou Panyu Gofront Dyeing & Finishing Machinery Manufacturer Ltd. | Chen X.,Guangzhou Panyu Gofront Dyeing & Finishing Machinery Manufacturer Ltd.
Jixie Gongcheng Xuebao/Journal of Mechanical Engineering | Year: 2016

It is required that the laser beam focus spot be controlled to follow and track the weld joint accurately during butt joint laser welding. An approach for detecting and tracking the micro-gap (whose width is less than 0.1 mm) weld position based on Kalman filtering during butt joint laser welding is presented. A magneto-optical sensor is used to capture the micro-gap weld image sequence, and the weld position coordinate is extracted by analyzing the gray scale gradient feature of images. The state equation and measurement equation for weld position are established based on the eigenvector derived from the weld position and displacement variables. Considering that the system dynamic noise and measurement noise are Gauss white noises with zero mean random distribution, a Kalman filter algorithm is established, which could reduce the influence of system and process noises on the weld position measurement. The optimal state estimation of weld centre ordinates could be obtained through the optimal state estimation of weld position under the least squares condition. Laser welding experimental results show that the proposed method could accurately predict the micro-gap weld position during the butt joint laser welding. © 2016 Journal of Mechanical Engineering.


Chen Y.,Guangdong University of Technology | Gao X.,Guangdong University of Technology | Xiao Z.,Guangzhou Panyu Gofront Dyeing & Finishing Machinery Manufacturer Ltd. | Chen X.,Guangzhou Panyu Gofront Dyeing & Finishing Machinery Manufacturer Ltd.
China Welding (English Edition) | Year: 2015

Keyhole is one of the important phenomena in high-power laser welding process. By studying the keyhole characteristic and detecting the seam offset during high-power fiber laser welding, an infrared sensitive high-speed camera arranged off-axis orientation of laser beam was applied to capture the dynamic thermal images of a molten pool. The 304 austenitic stainless steel plate butt joint welding experiment with laser power 10 kW was carried out. Through analyzing the keyhole infrared image, the weld position was calculated. Least squares method was used to determine the actual weld position. Image filtering technique was used to process the keyhole image, and the keyhole centroid coordinate were calculated. Also, least squares method was used to fit the keyhole centroid curve equation and establish a nonlinear continuous model which described the deviation between keyhole centroid and weld seam. The heat accumulation effect affected by the infrared radiation was analyzed to determine whether the laser beam focus spot deviated from the desired welding seam. Experimental results showed that the keyhole centroid has related to the seam offset, and can reflect the welding quality. Copyright: © 2015 Editorial Board of CHINA WELDING.


Gao X.,Guangdong University of Technology | Lin J.,Guangdong University of Technology | Xiao Z.,Guangzhou Panyu Gofront Dyeing & Finishing Machinery Manufacturer Ltd. | Chen X.,Guangzhou Panyu Gofront Dyeing & Finishing Machinery Manufacturer Ltd.
Hanjie Xuebao/Transactions of the China Welding Institution | Year: 2016

A BP neural network model based on ICA (Imperialist Competitive Algorithm) is proposed to recognize the arc welding penetration status. The weights and thresholds of the neural network are initialized using ICA which has the features of uneasy accessibility to local extremum and fast search speed. Then the BP algorithm is used to train the neural network. By capturing the images of the molten pool in welding process, three features of a molten pool image are processed. The features includes the weld pool area, weld pool width and the distance between the weld pool centroid and the bottom. These features are as the inputs of neural network to create the mapping relationship between the three features of molten pool and the weld penetration status, and eventually a predicted model of penetration status is established. Welding experimental results show that the welding penetration status can be accurately recognized using the ICA-BP neural network. © 2016, Editorial Board of Transactions of the China Welding Institution, Magazine Agency Welding. All right reserved.


Gao X.-D.,Guangdong University of Technology | Li G.-H.,Guangdong University of Technology | Xiao Z.-L.,Guangzhou Panyu Gofront Dyeing & Finishing Machinery Manufacturer Ltd. | Chen X.-H.,Guangzhou Panyu Gofront Dyeing & Finishing Machinery Manufacturer Ltd.
Guangxue Jingmi Gongcheng/Optics and Precision Engineering | Year: 2016

A multi-scale wavelet edge extraction algorithm and Principal Component Analysis-Back Propagation(PCA-BP) neural network classification model were proposed based on magneto-optical imaging to detect the welded defects such as sags, insufficient fusion on subsurface and welding misalignment. The visualization of detection and the classification of welded defects on the surface and subsurface of weldments were explored. Firstly, the weldments were magnetized by using an excitation magnetic field. Meanwhile, a magneto optical (MO) sensor based on the principle of Faraday magneto effect was used to acquire the MO images of weldments with welded defects. Then, a defect edge extraction algorithm with a better anti-noise property was investigated based on wavelet modulus maxima multi-scale information fusion theory to process MO images suffered from serious noises, low contrast and complex background. Finally, the PCA was taken to preprocess the column grey variables of MO images and 256 feature points of column variable of MO images which could characterize grey variable by 95% were obtained. Furthermore, these feature points were regarded as inputs of a three-layer BP neural network model to classify the welded defects. Experiment results show that the proposed method can be applied to detection of welded defects as mentioned above, and the accuracy of PCA-BP classification model has reached to 90.80%. © 2016, Science Press. All right reserved.


Chen Z.,Guangdong University of Technology | Gao X.,Guangdong University of Technology | Katayama S.,Osaka University | Xiao Z.,Guangzhou Panyu Gofront Dyeing & Finishing Machinery Manufacturer Co. | Chen X.,Guangzhou Panyu Gofront Dyeing & Finishing Machinery Manufacturer Co.
International Journal of Advanced Manufacturing Technology | Year: 2016

A method is introduced to investigate the interrelationships among welding penetration rate, spatter number, metallic vapor, and welding quality by simultaneously observing both top surface and bottom surface of low carbon steel weldment during high-power disk laser welding. Color image segmentation algorithm which is based on K-means clustering algorithm is used to process the image data. Different welding conditions including different weld power, weld speed and weld joint width are applied, and the microstructures of fusion part are also analyzed. Experiment results show that the color image segmentation algorithm is effectively for recognition of welding penetration condition, and welding quality is better when the penetration rate is around 74.5 %. © 2016 Springer-Verlag London


Gao X.,Guangdong University of Technology | Huang G.,Guangdong University of Technology | Xiao Z.,Guangzhou Panyu Gofront Dyeing & Finishing Machinery Manufacturer Ltd. | Chen X.,Guangzhou Panyu Gofront Dyeing & Finishing Machinery Manufacturer Ltd.
Hanjie Xuebao/Transactions of the China Welding Institution | Year: 2016

For micro-gap weld joint whose width is less than 0.1 mm, the weldments are magnetized by using an excitation magnetic field. Meanwhile, a magneto optical sensor based on the principle of Faraday magneto effect is applied to acquire the magneto optical images of weld joint. An edge detection method which fuses the wavelet multi-scale information of image is developed to deal with the magneto optical images of weld joint. Three layers wavelet decomposition of the weld magneto optical image is used to obtain wavelet image which contains high frequency information of weld edge. And then it fuses all high frequency images according to the richness of details which contain the high frequency information of each scale. By using wavelet modulus local maxima to detect the edge of fused image, the smooth weld edge with good noise immunity and continuity is obtained. The result of seam tracking test shows that the proposed method is an effective way to extract weld edge and is suitable for the image processing in micro-gap seam tracking based on magneto-optical imaging sensing. © 2016, Editorial Board of Transactions of the China Welding Institution, Magazine Agency Welding. All right reserved.


Gao X.,Guangdong University of Technology | Wu J.,Guangdong University of Technology | Xiao Z.,Guangzhou Panyu Gofront Dyeing & Finishing Machinery Manufacturer Ltd. | Chen X.,Guangzhou Panyu Gofront Dyeing & Finishing Machinery Manufacturer Ltd.
Hanjie Xuebao/Transactions of the China Welding Institution | Year: 2016

Accurate seam tracking is a prerequisite for laser welding with good quality. A seam tracking method based on Kalman filtering with colored noises is proposed to predict the seam deviationsin micro butt joint whose width is less than 0.05 mm. In the experiment, the weldments were magnetized by usingan excitation magnetic field. Meanwhile, a magneto-optical sensor based on the principle of Faraday magneto effect was applied to acquire the magneto-optical image of the weld joint. By analyzing the magneto-optical images of weld joint, the joint center position was extracted and defined as the state vector. Then the state equation and the measurement equation based on the weld joint center position were established. Considering that the system process noise was colored noise, the Sage adaptive filtering was used to lessen the noise influence. The innovation series was used to estimate the process noise variance matrix, and the weld joint position could be predicted accurately. Experimental results show that seam tracking accuracy can be improved effectively with self-adaptive Kalman filtering method. © 2016, Harbin Research Institute of Welding. All right reserved.

Loading Guangzhou Panyu Gofront Dyeing & Finishing Machinery Manufacturer Ltd collaborators
Loading Guangzhou Panyu Gofront Dyeing & Finishing Machinery Manufacturer Ltd collaborators