PHYSICAL VALIDATION OF JOB PLACEMENT OPTIMIZATION IN COOPERATIVE 3D PRINTING

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Date

2023

Authors

Mensch, Cole
Zhou, Wenchao
Sha, Zhenghui

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University of Texas at Austin

Abstract

Cooperative 3D printing (C3DP) is an emerging technology designed to overcome the limitations of traditional 3D printing, including speed and scalability. C3DP achieves this by partitioning prints into smaller jobs, e.g., chunks, and assigning them to a team of mobile 3D printers that work cooperatively in parallel allowing for autonomous additive manufacturing of large objects via a swarm-based system. Our prior work established a framework for optimizing job placement by connecting geometric partitioning algorithms with path planning and scheduling algorithms. However, this framework was not physically validated. In this paper, we present the first physical validation of the job placement algorithm by chunking and printing two objects using the proposed algorithm. The objects used in the test cases vary in size and complexity, from a small and simple object to a large object with intricate geometry. We demonstrate that our optimized placement algorithm provides results comparable to the physical C3DP system, providing a significant step forward in the practical implementation of C3DP technology.

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