Learning to Accelerate Decomposition for Multi-Directional 3D Printing

TL;DR

This paper introduces a learning-based method to speed up volume decomposition in multi-directional 3D printing, reducing computation time while maintaining high-quality results.

Contribution

It proposes a neural network scoring function trained on large-beam search results to accelerate the decomposition process in 3D printing.

Findings

Achieves approximately 3x faster decomposition computation.

Maintains similar quality of volume decomposition compared to large-beam search.

Validates the approach on a large dataset of 3D models.

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.