The root hump defect is easy to appear in the process of high power laser welding. Through the observation of welding experiment, it is found that there is obvious correlation between root hump defect and the character of keyhole and molten pool. Therefore, this paper proposes a method to monitor the root hump defect by identifying the keyhole and molten pool features in the welding process. In this method, image sensing technology and machine vision method are used to analyze and extract the keyhole and weld pool information in real time. The BP neural network algorithm is used to classify the welding states. It is found that adding the feature of weld pool length as input will greatly improve the recognition accuracy of the model.
In this paper, the differences of microstructure and mechanical properties of 2205 duplex stainless steel between preheating laser-induction hybrid welding (LIHW) and post-heating LIHW were compared by the optical microscope, nanoindentation, Vickers hardness and tensile machines. The results of the nano-indentation experiment show that the elastic modulus and hardness of ferrite are 42% and 64% larger than that of austenite, respectively. With the increase of LIHW heating power, the austenite proportion increases distinctly. Furthermore, the increase of the austenite results in the decrease of hardness and ultimate tensile stress. By quantifying the microstructure and mechanical properties, pre-heating LIHW exhibits greater superiority than post-heating LIHW with a heating power of 49.1 kW, laser power of 3 kW and welding speed of 30 mm/s.
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