Enhancing Vat Photopolymerization 3D Printing Through Image Correlation-Based Adaptive Slicing

Vat photopolymerization 3D printing is a widely adopted technique for producing high-resolution parts. However, optimizing the printing process to balance quality and efficiency remains challenging. In this study, we present an adaptive slicing method for a liquid crystal display (LCD)- based 3D printer using an image correlation algorithm. Initially, STL models were sliced at a fixed layer height of 0.025 mm, generating sequential images. Subsequently, an image correlation algorithm was applied to analyse geometric variations between slices, allowing for adaptive adjustment of slicing heights. Two slicing approaches were compared: the conventional fixed-layer slicing and the proposed adaptive slicing. Key parameters, such as the number of printing layers and total printing time, were calculated for both approaches. Both models were fabricated, and their mechanical properties were evaluated using tensile and surface tests. Results revealed that the adaptive slicing method significantly reduced printing time while maintaining comparable mechanical properties to the fixed-layer approach. The reduction in printing layers without compromising surface quality demonstrates the potential of adaptive slicing for efficient vat photopolymerization. This study highlights the integration of image correlation algorithms as a promising advancement for smart slicing strategies, offering a pathway to optimize the trade-off between resolution and efficiency in 3D printing.