Limiting defect in vat photopolymerization via visual-guided in-situ repair

Abstract

Vat photopolymerization 3D printing has gained significant attention due to its fast printing speed and high precision. However, the absence of effective quality assurance methods greatly limits its applications. Current vat photopolymerization cannot deal with commonly occurring defects during the printing process, such as random bubbles and resin underfill, making it challenging to consistently produce products that match designed geometry and functions. To address this, we propose an innovative vat photopolymerization solution via visual-guided in-situ repair to effectively eliminate printing defects. By utilizing an enhanced YOLOv5 network and K-means algorithm, real-time detection of bubbles and resin underfill can be achieved using image analysis. The optimal method for defect repair was then automatically generated via the adaptive scraper control and the repair slice edge smoothing generation algorithm, which was immediately received by hardware to adjust the ongoing printing parameters without interrupting the continuous printing process. Experimental results demonstrate that the aforementioned strategy can accurately differentiate typical defects such as bubbles and resin underfill, and precisely carry out in-situ defect repairs. Comparisons between repaired samples and defect-free samples show minimal differences in surface morphology and fracture strength (¡0.6%). The proposed solution is also applicable to various precursors. Clearly, the strategy above offers a universal approach to avoid defects in vat photopolymerization, which effectively enhances printing efficiency, reduces material wastage, and ensures the quality, accuracy, and reliability of printed products.