The penetration depth of welding seam can reflect welding quality fundamentally, during the gas metal arc welding (GMAW) process, the penetration depth of welding seam fluctuates over time. At present, it lacks of reliable sensing method to predict penetration depth fluctuation accurately in real time. To solve the above problem, in this paper, proposing a real-time prediction method for weld penetration mode and depth based on two-dimensional visual characteristics of weld pool, establishing a monocular vision sensing system, extracting the area, length and width of weld pool as key two-dimensional visual characteristics. Taking the extracted current frame characteristics of weld pool as the input, the weld penetration of welding seam corresponding to current frame as the output, based on support vector machine (SVM) and back propagation (BP) neural network respectively, the real-time prediction models for weld penetration mode and depth were established. The predicted results show that the established models can accurately predict the penetration mode and penetration depth of welding seam in real time.

In the gas-metal-arc (GMA) additive manufacturing process, the shape of the molten pool, the temperature field of the workpiece and the heat dissipation conditions change with the increase of cladding layers, which can affect the dimensional accuracy of the workpiece; hence, it is necessary to monitor the additive manufacturing process online. At present, there is little research about formation-dimension monitoring in the GMA additive manufacturing process; in this paper, weld reinforcement prediction in the GMA additive manufacturing process was conducted, the visual-sensing system for molten pool was established, and a laser locating system was designed to match every frame of the molten pool image with the actual weld location. Extracting the shape and location features of the molten pool as visual features, on the basis of a back-propagation (BP) neural network, we developed the prediction model for weld reinforcement in the GMA additive manufacturing process. Experiment results showed that the model could accurately predict weld reinforcement. By changing the cooling time between adjacent cladding layers, the generalization ability of the prediction model was further verified.

Connecting a variable groove weldment is always challenging, and it is necessary to monitor the course of the work and optimize the welding process parameters in real time to ensure the final welding forming quality. Welding penetration is an important index to appraise the welding forming quality; the visual sensing method for molten pool is the main method for detecting the weld penetration, but its detection accuracy is affected by the arc light. In this paper, a welding penetration sensing method for variable groove weldments based on the welding temperature field distribution is proposed. Firstly, a set of temperature field measurement system for a weldment is developed by means of an infrared sensor. Secondly, in the direction perpendicular to the welding direction, a linear temperature distribution feature extraction algorithm based on Gaussian fitting is studied; in the direction parallel to the welding direction, the linear temperature distribution feature extraction algorithm based on the thermal cycle parameters is studied, and the feasibility of using the extracted linear temperature distribution features to identify the weld penetration of a variable groove weldment is analyzed. Finally, taking the extracted linear temperature distribution features as input, using an artificial neural network, the prediction model for the welding penetration of a variable groove weldment is established. The experimental results showed that the weld penetration sensing method put forward in this paper can realize high-precision weld penetration sensing and has high reliability, which solves the problem that weld penetration sensing is affected by arc light to a great extent.

Real-time monitoring of welding temperature field during welding process is important for improving the weld forming quality, At present, the numerical simulation method is mostly used to research on the welding temperature field of Cold Metal Transfer (CMT) process. In this paper, a set of CMT welding temperature field detection system is designed based on color Charge Coupled Device (CCD), the system is mainly consist of optical lens, color CCD, Field Programmable Gate Array (FPGA) and computer. According to CMT welding current, FPGA controls color CCD acquire weld pool image, the red and green primary color output images of color CCD are used for colorimetric temperature measurement, after calibrating the welding temperature field detection system, the equation for calculating the welding temperature field is obtained by least square fitting. The feasibility of the designed CMT welding temperature field detection system is proved by the experiment of detecting the temperature field variation of weld pool in the basic period of CMT process.

The temperature field distribution of molten pool affects the quality of welding process directly, study on the temperature field distribution of molten pool to improve the quality of welding process is of great significance. The temperature field distribution of molten pool under Cold Metal Transfer(CMT) welding process is studied in this paper, a temperature field detecting system for molten pool is designed based on color thermometry, the system is mainly composed of spectroscope, filter, Charge Coupled Device(CCD) camera, computer and Field Programmable Gate Array(FPGA). The FPGA triggers the CCD camera when the welding current is at the base value under the CMT welding process, in this way, the acquired molten pool images are almost unaffected by arc light. The formula for calculating the temperature field of molten pool is derived, and the experimental verification is carried out. The experimental results show that: Under the CMT welding process, the designed temperature field detecting system is almost free from the influence of arc light, and it can detect the temperature field distribution of molten pool in real time.