![]() Current approaches that employ decomposition do so for volume packing, support reduction, or optimisation of printing time in serial the aim of this research is to take advantage of the theoretical increased throughput afforded by multiple 3D printers to improve fabrication times in parallel. In this paper a new approach is presented for improving the fabrication time of producing models on commercial FFM 3D printers by involving the use of multiple 3D printers in parallel. This creates a need for an algorithm that is specifically tailored for optimising printing in parallel to make efficient use of these resources, by devising logic that sees decomposition in terms of balancing printer utilisation, rather than minimising an overall aggregate printing time of parts. 1).īut, in the context of an increasing availability of 3D printers, as is often encountered in lab settings, the decomposition research could be suboptimal in its use of the available resources, causing some printers to be doing large quantities of work and others to be sitting idle for significant lengths of time. Current research in the area has largely focused on the computational optimisation of these parameters in various ways, often in the context of specific domains (Fig. Traditional methods of decreasing the time to print larger models usually involve some combination of increasing the printing rate at the cost of decreasing quality and reducing the density of the obscured internal space, or altering the orientation of the model to reduce support structures. Larger, simpler, and more symmetric objects exhibited more significant and reliable improvements in fabrication duration at larger amounts of parallelisation than smaller, more complex, or more asymmetric objects. The algorithm was subjected to a range of models and a varying quantity of printers in parallel, with printer parameters held constant, and yielded mixed results. Experimental evaluation of the algorithm was performed to compare our approach to printing models normally (“in serial”) as a control. To achieve this, a decomposition framework was designed that combines recursive symmetric slicing with a hybrid tree-based analytical and greedy strategy to optimally minimise the maximum volume of subparts assigned to the set of printers. This was approached as a problem akin to the parallel delegation of computation tasks in a multi-core environment, where optimal performance involves computation load being distributed as evenly as possible. A novel approach to 3D printing is introduced that attempts to exploit this as a means of significantly increasing the speed of printing models. With improved hardware costs increasing printer availability, more situations can arise involving a multitude of printers, which offers substantially more throughput in combination that may not be best utilised by current decomposition approaches. Current research in 3D printing focuses on improving printing performance through various techniques, including decomposition, but targets only single printers.
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