Learning to Accelerate Decomposition for Multi-Directional 3D Printing
This work addresses a computational bottleneck in 3D printing optimization, enabling faster decomposition for reduced support structures, though it is incremental as it builds on existing beam-guided search methods.
The paper tackles the slow computation of beam-guided search for multi-directional 3D printing decomposition by proposing a learning framework that accelerates it, achieving around 3x computational speed while maintaining similar quality.
Multi-directional 3D printing has the capability of decreasing or eliminating the need for support structures. Recent work proposed a beam-guided search algorithm to find an optimized sequence of plane-clipping, which gives volume decomposition of a given 3D model. Different printing directions are employed in different regions to fabricate a model with tremendously less support (or even no support in many cases).To obtain optimized decomposition, a large beam width needs to be used in the search algorithm, leading to a very time-consuming computation. In this paper, we propose a learning framework that can accelerate the beam-guided search by using a smaller number of the original beam width to obtain results with similar quality. Specifically, we use the results of beam-guided search with large beam width to train a scoring function for candidate clipping planes based on six newly proposed feature metrics. With the help of these feature metrics, both the current and the sequence-dependent information are captured by the neural network to score candidates of clipping. As a result, we can achieve around 3x computational speed. We test and demonstrate our accelerated decomposition on a large dataset of models for 3D printing.