No abstract

Polylactide acid (PLA) is one of the most used plastics in extrusion-based additive manufacturing (AM). Although it is bio-based and in theory biodegradable, its recyclability for fused filament fabrication (FFF) is limited due to material degradation. To better understand the material’s recyclability, blends with different contents of recycled PLA (rPLA) are investigated alongside a coextruded filament comprised of a core layer with high rPLA content and a skin layer from virgin PLA. The goal was to determine whether this coextrusion approach is more efficient than blending rPLA with virgin PLA. Different filaments were extruded and subsequently used to manufacture samples using FFF. While the strength of the individual strands did not decrease significantly, layer adhesion decreased by up to 67%. The coextruded filament was found to be more brittle than its monoextruded counterparts. Additionally, no continuous weld line could be formed between the layers of coextruded material, leading to a decreased tensile strength. However, the coextruded filament proved to be able to save on master batch and colorants, as the outer layer of the filament has the most impact on the part’s coloring. Therefore, switching to a coextruded filament could provide economical savings on master batch material.

The biggest advantage of Additive Manufacturing is the individualization of products. Mass Customization is well known as a promising future application. The use of Additive Manufacturing for assembly groups is mostly not reasonable, however combining it with conventional manufacturing processes can lead to new opportunities.This paper works out concepts to join, by using similar material combinations, an injection molded part with an additive deposited geometry by the Fused-Deposition-Modeling (FDM) process. Therefore, two of the main industrially used polymers, acrylonitrile butadiene styrene (ABS) and polypropylene (PP), are selected for further study. In particular, this investigation focuses on the procedural potentials and surface preparation of the injection molded part. By the variation of adhesive bonding, the fusion of similar materials can be identified and tested in several series of testing.First in general a direct joining function by the FLM process will be tested. After proving this hypothesis, the results will be summarised in a recommendation of joining similar materials, which are manufactured in different ways.

Additive manufacturing has revolutionized prototyping and small-scale production in the past years. By creating parts layer by layer, a tool-less production technology is established, which allows for rapid adaption of the manufacturing process and customization of the product. However, the geometric freedom of the technologies comes with a large number of process parameters, especially in Fused Deposition Modeling (FDM), all of which influence the resulting part’s properties. Since those parameters show interdependencies and non-linearities, choosing a suitable set to create the desired part properties is not trivial. This study demonstrates the use of Invertible Neural Networks (INN) for generating process parameters objectively. By specifying the desired part in the categories of mechanical properties, optical properties and manufacturing time, the demonstrated INN generates process parameters capable of closely replicating the desired part. Validation trials prove the precision of the solution with measured properties achieving the desired properties to up to 99.96% and a mean accuracy of 85.34%.

No abstract