Towards scalable and efficient GPU-enabled slicing acceleration in continuous 3D printing

Recently, continuous 3D printing, a revolutionary branch of legacy additive manufacturing, has made its two-order time efficiency breakthrough in industrial manufacturing. As its manufacturing technique advances rapidly, the prefabrication to slice the 3D object into image layers becomes potential to impede further improvement of production efficiency. In this paper, we present two scalable and efficient graphic processing unit (GPU) enabled schemes, i.e., pixelwise parallel slicing and fully parallel slicing, to accelerate the image-projection based slicing algorithm in continuous 3D printing. Specifically, the pixelwise approach utilizes the pixel-level parallelism and exploits the in-shared-memory computing on GPU. The fully parallel method aggressively expands the parallelism on both triangle mesh size and slicing layers. The thread-level priority competing issue, resulting from full parallelism, is addressed by a critical area using atomic operation. Experiments with real 3D object benchmarks show that our pixelwise parallel slicing can gain one order of magnitude runtime reduction to CPU, and the fully parallel slicing achieves two orders improvement. We also evaluate the scalability of both proposed schemes.