Abstract
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.
Highlights
Additive manufacturing technology is based on the idea of discreteness and accumulation, using material to stack layer by layer, forming a three-dimensional entity [1]
Gas-metal-arc (GMA) additive manufacturing is the processing technology that uses an electric arc as a heat source to melt metal wire and stacks, forming a metal workpiece [3,4], Yanhu Wang et al [5,6] pointed out that additive manufacturing based on arc welding has the outstanding advantages of low cost and high efficiency, and can be widely used in many fields
In the GMA additive manufacturing process, the trigger module gave out a fixed frequency signal to control the color and monochrome cameras at the same time; by observing the location of the laser point in every frame of the monochrome image, precisely matching every frame of color molten pool images with the specific weld location
Summary
Additive manufacturing technology is based on the idea of discreteness and accumulation, using material to stack layer by layer, forming a three-dimensional entity [1]. Gas-metal-arc (GMA) additive manufacturing is the processing technology that uses an electric arc as a heat source to melt metal wire and stacks, forming a metal workpiece [3,4], Yanhu Wang et al [5,6] pointed out that additive manufacturing based on arc welding has the outstanding advantages of low cost and high efficiency, and can be widely used in many fields. GMA additive manufacturing as the research object, used a color CCD to collect molten pool images and extract the shape and position features of the molten pool in real time; by means of a neural network, established the prediction model for weld reinforcement, and realized the online monitoring of weld reinforcement in the additive manufacturing process, which has important guiding significance for the subsequent control of forming size
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