Accelerating Additive Manufacturing Slicing: A GPU-Based Parallel Algorithm for Large and Complex Mesh Models^*

Efficient slicing of massive data remains a significant challenge in additive manufacturing. To address computer memory limitations and enhance slicing efficiency, this study presents a novel approach combining batch processing of large-scale mesh models with a GPU-accelerated parallel slicing algorithm. The proposed method partitions mesh model files, which are typically too large for single memory allocation, into multiple sub-models based on the slicing direction. During sub-model processing, an optimized edge-labeling algorithm is implemented to topologically mark all edges within each sub-model. The slicing operation is then executed in parallel across sub-models using GPU acceleration through OpenCL, significantly improving computational efficiency. The individual slicing results are subsequently integrated to generate the final output. Theoretically, this algorithm eliminates memory constraints on mesh model size while maintaining high slicing efficiency. Comparative experiments with industry-standard software Magics and Cura demonstrate the superiority of our method. The proposed algorithm successfully processes large-scale mesh models that exceed the capacity of both commercial solutions. Furthermore, it achieves a remarkable 80% reduction in slicing time for complex models compared to Magics and Cura, demonstrating both the feasibility and superior efficiency of our approach.