An experimental setup of acquiring the coaxial visual image of the molten pool and keyhole in high power Nd:YAG laser welding is introduced in this paper. It is one of the most difficult problems in acquiring coaxial ...An experimental setup of acquiring the coaxial visual image of the molten pool and keyhole in high power Nd:YAG laser welding is introduced in this paper. It is one of the most difficult problems in acquiring coaxial image that the coaxial imaging signal of molten pool and keyhole must be separated from the laser beam with high power. This problem was resolved by designing a dichroitic spectroscope. The characteristics of imaging signal were analyzed and the coaxial image of molten pool and keyhole was acquired. A smoothing filter and a homomorphic filter were designed to remove the low frequency noise and to enhance the image according to the characteristics of imaging signal. At last, edges of molten pool and keyhole were detected and extracted based on image segmentation with threshold.展开更多
In manual welding process, skilled welders can adjust the welding parameters to ensure the weld quality through their observation of the weld pool surface. In order to acquire useful information of the weld pool for c...In manual welding process, skilled welders can adjust the welding parameters to ensure the weld quality through their observation of the weld pool surface. In order to acquire useful information of the weld pool for control of the welding process and realizing the automatic welding, the measurement system of DB-GMA W process was established and the weld pool image was obtained by passive vision. Then, three image processing algorithms, Sobel, Canny, and pulse coupled neural network （PCNN） were detailed and applied to extracting the edge of the DB-GMA weld pool. In addition, a scheme was proposed for calculating the length, maximum width and superficial area of the weld pool under different welding conditions. The compared results show that the PCNN algorithm can be used for extracting the edge of the weld pool and the obtained information is more useful and accurate. The calculated results coincide with the actual measurement well, which demonstrates that the proposed algorithm is effective, its imaging processing time is required only 20 ms, which can completely meet the requirement of real-time control.展开更多
It is difficult to acquire satisfied weld pool image by CCD sensor during gas metal arc welding（ GMAW） , for arc disturbs violently, welding current is great and working frequency is high. By using CMOS vision senso...It is difficult to acquire satisfied weld pool image by CCD sensor during gas metal arc welding（ GMAW） , for arc disturbs violently, welding current is great and working frequency is high. By using CMOS vision sensor to GMA W process, the vivid weld pool image is collected at any time, furthermore, whose gray compression ratio is controllable by sensor hardware circuit developed. Acquired weld pool image is firstly pre-processed by using Wiener filter and Ostu threshold segmentation algorithm. Subsequently separation between weld pool image and cathode mist region is conducted by means of mathematical morphological algorithm, and the edge of weld pool image is extracted by using Prewitt algorithm.展开更多
Laser welding is one of high efficiency, high energy density welding methods. Quality control should be applied to ensure good welding quality. Weld pool and keyhole contains abundant information of welding quality. G...Laser welding is one of high efficiency, high energy density welding methods. Quality control should be applied to ensure good welding quality. Weld pool and keyhole contains abundant information of welding quality. Good image processing algorithm is necessary in quality control system based on visual sensing. Aiming at the image captured by a coaxial visual sensing system for laser welding, an image processing algorithm is designed. An edge predicting method is proposed in image processing algorithm which is based on the fact that the local shape of weld pool can be fitted to a circle. The results show that the algorithm works well. It lays solid foundation for further quality control in laser welding.展开更多
文摘An experimental setup of acquiring the coaxial visual image of the molten pool and keyhole in high power Nd:YAG laser welding is introduced in this paper. It is one of the most difficult problems in acquiring coaxial image that the coaxial imaging signal of molten pool and keyhole must be separated from the laser beam with high power. This problem was resolved by designing a dichroitic spectroscope. The characteristics of imaging signal were analyzed and the coaxial image of molten pool and keyhole was acquired. A smoothing filter and a homomorphic filter were designed to remove the low frequency noise and to enhance the image according to the characteristics of imaging signal. At last, edges of molten pool and keyhole were detected and extracted based on image segmentation with threshold.
基金This work is supported by the National Natural Science Foundation of China under Grant No. 61365011.
文摘In manual welding process, skilled welders can adjust the welding parameters to ensure the weld quality through their observation of the weld pool surface. In order to acquire useful information of the weld pool for control of the welding process and realizing the automatic welding, the measurement system of DB-GMA W process was established and the weld pool image was obtained by passive vision. Then, three image processing algorithms, Sobel, Canny, and pulse coupled neural network （PCNN） were detailed and applied to extracting the edge of the DB-GMA weld pool. In addition, a scheme was proposed for calculating the length, maximum width and superficial area of the weld pool under different welding conditions. The compared results show that the PCNN algorithm can be used for extracting the edge of the weld pool and the obtained information is more useful and accurate. The calculated results coincide with the actual measurement well, which demonstrates that the proposed algorithm is effective, its imaging processing time is required only 20 ms, which can completely meet the requirement of real-time control.
文摘It is difficult to acquire satisfied weld pool image by CCD sensor during gas metal arc welding（ GMAW） , for arc disturbs violently, welding current is great and working frequency is high. By using CMOS vision sensor to GMA W process, the vivid weld pool image is collected at any time, furthermore, whose gray compression ratio is controllable by sensor hardware circuit developed. Acquired weld pool image is firstly pre-processed by using Wiener filter and Ostu threshold segmentation algorithm. Subsequently separation between weld pool image and cathode mist region is conducted by means of mathematical morphological algorithm, and the edge of weld pool image is extracted by using Prewitt algorithm.
文摘Laser welding is one of high efficiency, high energy density welding methods. Quality control should be applied to ensure good welding quality. Weld pool and keyhole contains abundant information of welding quality. Good image processing algorithm is necessary in quality control system based on visual sensing. Aiming at the image captured by a coaxial visual sensing system for laser welding, an image processing algorithm is designed. An edge predicting method is proposed in image processing algorithm which is based on the fact that the local shape of weld pool can be fitted to a circle. The results show that the algorithm works well. It lays solid foundation for further quality control in laser welding.
