Parallelobox: Improved Decomposition for Optimized Parallel Printing using Axis-Aligned Bounding Boxes

TL;DR

Parallelobox introduces an axis-aligned bounding box-based decomposition method to optimize parallel 3D printing, improving print time, feasibility, and aesthetics over existing techniques.

Contribution

It presents a novel bounding box-based decomposition approach that enhances parallel printing efficiency using a metaheuristic process and mesh clipping.

Findings

Parallelobox outperforms existing methods in print time metrics.

The approach improves feasibility and aesthetics of decomposed models.

Experimental results show significant efficiency gains across various models.

Abstract

Much contemporary research in additive manufacturing focuses on breaking down models into constituent parts in the pursuit of various factors, such as printability of large models in smaller printing volumes, or reduction of support structures. Newer research has begun to focus on using these decomposition processes for printing models across multiple printers in parallel. We present a novel approach to this that incorporates axisaligned bounding boxes as height fields to improve the characteristics of decomposition, including printing time, feasibility, and aesthetics. By expanding these bounding boxes according to a parallel printing objective, with additional improved efficiency from a metaheuristic process, these boxes can then be used for rapid decomposition using simple out-of-the-box mesh clipping operations. This algorithm is experimentally evaluated across a range of models against two other contemporary approaches to parallel printing that use more rudimentary techniques, such as recursive symmetry and cube skeletonization. Parallelobox outperformed each of these across a range of sample models on the basis of a parallel printing time metric using simulated 3D printing to compute the results