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.

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. INDEX TERMS Penetration mode, penetration depth, visual characteristics, SVM, neural network.

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