4.8 Article

Load balancing for multi-beam additive manufacturing systems

Journal

ADDITIVE MANUFACTURING
Volume 74, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.addma.2023.103708

Keywords

Load balancing; Additive manufacturing; Optimization

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As powder bed fusion (PBF) additive manufacturing (AM) progresses, system configurations are shifting towards unconventional configurations to increase throughput. The inclusion of multiple heat sources increases the complexity of control schemes and load balancing becomes crucial. This paper introduces high-performing load balancing methods for multi-beam systems of any complexity, enabling on-the-fly load balancing in case of beam failures and improving system robustness.
As powder bed fusion (PBF) additive manufacturing (AM) becomes a more mature field, system configurations are gradually moving away from the classic single heat source, layer-by-layer system configurations towards unconventional system configurations that offer higher throughput. Higher throughput systems allow PBF systems to be considered for a larger variety of industrial applications. However, the inclusion of multiple heat sources, or beams, also increases the complexity of the control schemes needed. For multi-beam systems with overlapping fields of view, the distribution of workload, or load balancing, across these beams directly affects the total print time for a build. Additionally, the probability of any beam failing in a multi-beam system increases with the number of beams. While manual methods of load balancing and dealing with beam failures are reasonable for current generation multi-beam systems, as system configurations become more complex, manual methods will become prohibitively inefficient. This paper introduces two different ways to load balance multi-beam systems of various configuration types, regardless of their complexity, which are highly performant. A consequence of this performance is the enablement of on-the-fly load balancing in the event a beam fails, thus improving system robustness.

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