Abstract
Laser metal deposition (LMD) is an additive manufacturing technique, whose performance can be influenced by a considerable number of factors and parameters. Typically, a powder is carried by an inert gas and sprayed by a nozzle, with a coaxial laser beam passing through the nozzle and overlapping the powder flow, thereby generating a molten material pool on a substrate. Monitoring the evolution of this process allows for a better comprehension and control of the process, thereby enhancing the deposition quality. As the metal additive manufacturing mechanism has not yet been elucidated, it is not clear how process parameters affect material properties, molding accuracy, and molding efficiency. When cladding is performed under uncertain conditions, a molded part with poor material properties and dimensional accuracy is created. In this paper, we propose a method for high efficiency molding by controlling the distance between the head nozzle and the molten pool in real time. The distance is identified by an originally developed sensor based on a triangulation method. According to the distance, the head nozzle is automatically controlled into the optimum position. As a result, an ideal molding process can be generated, so that high efficiency molding and high-quality material properties can be obtained. Experimental results show that continuing deposition at the optimum distance assists in achieving deposition efficiency and dimensional accuracy. According to the specific experimental results of this method, the modeling efficiency was increased by 27% compared to the method without correction, and the modeling was successful with an error within 1 mm.
A Real-Time Iterative Machine Learning Approach for Temperature Profile Prediction in Additive Manufacturing Processes
