Adaptive Deposit Compensation of Construction Materials in a 3D Printing Process

Additive Manufacturing (AM), known as 3D Printing (3DP), has been widely implemented in the industry due to its advantage of free design and high efficiency in rapid prototyping. Recently, 3D Construction Printing (3DCP) has become an emerging topic. As a multidisciplinary subject, it involves a complex system that consists of multiple subsystems, the uncertainty of material behaviour also bring risks to the printing quality. Hence, the main issue of 3DCP lies in the online monitoring and control of the printing process quality. This paper presents a combined approach for online 3D construction material printing quality monitoring and adaptive compensation for printing layer. The width of freshly printed filament can be adopted to characterize the printing quality. A vision system based on Deep Learning is developed to detect automatically the filament width deviation. Based on the results of the vision system, a real time adaptive compensation of the nozzle travel speed is realized to maintain the quality deposit performance. The proposed approach is validated by on site printing experiments.