Learning to Accelerate Decomposition for Multi-Directional 3D Printing

• URL: http://arxiv.org/abs/2004.03450v3
• Date: Sat, 18 Jul 2020 04:50:55 GMT
• Title: Learning to Accelerate Decomposition for Multi-Directional 3D Printing
• Authors: Chenming Wu, Yong-Jin Liu, Charlie C.L. Wang
• Abstract summary: 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. We propose a learning framework that can accelerate the beam-guided search by using a smaller number of the original beam width.
• Score: 31.658049974100088
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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