Coordinated control method of layer width and reinforcement height in arc additive manufacturing based on deep learning

Abstract

The invention relates to a method for synergistic control of layer width and reinforcement in arc additive manufacturing based on deep learning, and belongs to the technical field of precision welding. It includes the following steps: Step 1: Collect the molten pool image through the arc additive manufacturing detection system, and extract the layer width and reinforcement data of the weld; Step 2: Based on the layer width and reinforcement data extracted in step 1, construct a deep learning-based The regression model of the weld layer width and reinforcement height; Step 3: Input the real-time collected front and side molten pool images into the regression model to obtain the expected weld layer width and reinforcement height, adjust the welding current and control the welding current through the active disturbance rejection control algorithm The layer width and reinforcement of the weld. The welding current in step 3 is controlled by the welding power supply, which is connected to the computer through the control cabinet. The invention uses the visual information of the molten pool to quantitatively predict the offset of the cladding layer in the process of adding materials, so as to adjust the actual position of the welding torch in the process of adding materials, thereby obtaining a good shape of the molten pool and improving the welding quality.

Description

technical field [0001] The invention relates to a method for collaborative control of layer width and reinforcement in arc additive manufacturing based on deep learning, and belongs to the technical field of precision welding. Background technique [0002] Additive manufacturing technology has been widely used in the fields of automobile, aerospace, architectural design and biomedicine, etc. Arc additive manufacturing technology uses welding arc as fusion energy to melt metal wire, which has higher material utilization rate compared with other additive manufacturing methods High, high deposition efficiency, environmental protection and other characteristics have attracted extensive attention in recent years. However, in the process of arc additive manufacturing, as the number of cladding layers increases, there will be problems such as serious heat accumulation, poor heat dissipation conditions, overheating of the molten pool, difficulty in solidification, and difficulty in ...

AI Technical Summary

Problems solved by technology

However, in the process of arc additive manufacturing, as the number of cladding layers increases, there will be problems such as serious heat accumulation, poor heat dissipation conditions, overheating of the molten pool, difficulty in solidification, and difficulty in controlling the shape of the cladding layer. The robot monitors the welding situation in real time and adjusts the welding process parameters in time to improve the welding quality

In the current research, the real-time feedback control in the arc additive manufacturing process is still relatively insufficient.

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