Image decomposition accelerates dynamic network modeling for in situ monitoring of bio-mimic wing printing processes

Bio-mimic wings are crucial constituents of Flapping-Wing Micro Air Vehicles, and the quality of such products plays a vital role in missions involving area exploration, aerial imaging and monitoring. Fused filament fabrication, as a versatile manufacturing approach, can be adopted to print single-layer artificial wings in an additive way. In such processes, quality of the printed wings is determined by the percentage of covered area, contingent on the vertical distance between the printing nozzle and the substrate. Even when properly calibrated, this distance is subject to variations due to the exceeding sensitivity to process drift, including vibration of the platform. In situ image analysis provides rapid guidance for process drift detection and assessment of the printed wing structures. While dynamic network analysis has been applied to quality assurance of steaming images, the computational overhead has made it unrealistic for monitoring high-definition images, particularly in the detection of images with incipient or tiny defects. In this study, we investigate image decomposition techniques, including wavelet transformation and bi-dimensional empirical mode decomposition (BEMD), to dissolve the original images into a series of basis images, which significantly reduces the number of communities in the network representation of images and diminishes the computational cost. Lastly, network generalized likelihood ratio control chart is developed to characterize anomalous variations in structures of the images. Our investigation shows that wavelet and EMD elevate the detection accuracy by 36.7% and 63.3%, while reducing the processing time by 43.5% and 73.9%, respectively, compared to the dynamic network approach without decomposition.