Korea Advanced Institute of Science and Technology

Ikgeun Jeon

Hoon Sohn

Peipei Liu

A method of feedback controlling a 3D printing process in real time, and a system therefor are disclosed. The method includes collecting big data, generated through 3D printing experiments, related to process variables of 3D printing, measurement signals, and 3D printing quality of the 3D printing object; building an artificial neural network model by performing machine-learning based on the collected big data; evaluating whether or not a 3D printing quality of the 3D printing object is abnormal in real time based on an actual measurement signal of the 3D printing object and the artificial neural network model; and feedback controlling printing quality of the 3D printing object in real time based on the evaluation result of whether or not the 3D printing quality of the 3D printing object is abnormal.

Method of feedback controlling 3D printing process in real-time and 3D printing system for the same

Disclosed are a method and apparatus of estimating a depth of a molten pool formed during a 3D printing process, and a 3D printing system. A surface temperature of the molten pool is measure by taking a thermal image of a laminated printing object during the 3D printing process with a thermal imaging camera. The measured surface temperature is compared with a melting point of the base material to determine a surface boundary of the molten pool. The maximum lengths in x-axis and y-axis directions of a surface region of the molten pool defined by the surface boundary of the molten pool are determined as a length and a width of the surface of the molten pool, respectively. A maximum depth in the z-axis direction of the molten pool is determined in real time based on the length and width of the surface region of the molten pool.

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Daejeon, South Korea

Ikgeun Jeon