Using feedback control of thermal history to improve quality consistency of parts fabricated via large-scale powder bed fusion

Process inconsistency in additive manufacturing (AM) leads to irregular quality of the final parts and obstructs its broader adoption in critical structural parts manufacturing. Especially in the manufacture of sizable components, detecting process changes in a real-time and accurate manner for potential corrective operations is crucial. This study aims to develop a feedback control system to reduce inconsistent part quality caused by heat accumulation differences. There are two significant challenges. 1) Thermal images suffer from a low signal-to-noise ratio. 2) Discrete local sintering information should be able to indicate the adjustments to the global temperature field. To tackle these challenges, a feedback model based on a multi-input neural network is proposed to evaluate the sintering status accurately by integrating the thermal history and process features. Subsequently, a layerwise feedback control strategy is proposed to process the discrete sintering status into the variation trend of part quality and ensure that the material has the desired thermal history during sintering. A controlled experiment is used to demonstrate the effectiveness of the proposed approach compared with its traditional counterparts, and the result illustrates the elimination of differences in heat accumulation by the proposed method.