Abstract
Fostering the development of additive manufacturing (AM) in the context of mass production is a key factor to ensure its adoption in the industry. It should be remembered that this technology intrinsically makes it possible to produce parts with unexpected complexities in terms of shape and structure, but this comes at a price: time. To overcome this productivity barrier, AM technology providers are developing 3D printing machines with high-speed performance and mass reproduction means in a single run. Although such trends can be seen as a natural evolution of this technology with respect to current consumption patterns, it still remains a scientific issue on production planning to be tackled. The objective is to address the on-demand production planning of different AM parts in FabLabs composed of unrelated parallel 3D printers. A novel framework is introduced to consider part orientation, path planning, and part-to-printer assignment, with a specific focus on fused filament fabrication technique. By targeting a minimum production time, it exhibits reasoning algorithms implemented in a Python application. A case study with a batch of six non-identical parts and two fused filament fabrication 3D printers is introduced to illustrate the added value of the framework and its operational side.
Highlights
Additive manufacturing (AM) is currently considered as a key technology that brings together processes and techniques to produce—in a layer-by-layer deposition mode—objects or assemblies [1]
This is the case for the part orientation step, to which an open-source Python application called Tweaker—which is able to find an optimal orientation of an object on a fused filament fabrication (FFF) 3D printer— has been adopted [31]
Developed as a Cura backend from Ultimaker, it is widely used in the FFF community since it works with any kind of FFF 3D printer and is suitable enough to analyze parts’ geometries, define trajectory paths, estimate build times, and generate machines’ instructions
Summary
Additive manufacturing (AM) is currently considered as a key technology that brings together processes and techniques to produce—in a layer-by-layer deposition mode—objects or assemblies [1]. With AM, the complexity in terms of shape and structure is free, but the printing time can be significant, which breaks with current mass production rates in the industry [3] This is the reason why AM has been mainly used for rapid prototyping purposes and complex parts (with added value) manufacturing. A part of the concerns is devoted to increasing productivity to meet the industrial needs for mass customization/personalization In such a context, efforts are currently being made to produce a batch of parts, while maintaining the same AM technique and machine, leading to the development of 3D printers with better performances and higher printing volumes [4]
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