(567c) Modeling of Poly(ether imide) in Material Extrusion Additive Manufacturing

Additive manufacturing (AM), is a manufacturing modality that allows the production of small scale, customized parts quickly and cheaply. Material extrusion (MatEx) AM is one of the most popular types of AM. This process uses a filament feedstock to produce a part by depositing thin roads of material in 2D layers which are then stacked in the desired geometry to create a 3D shape. However, this method of manufacturing is currently limited primarily to the rapid production of prototypes and physical models. The ultimate goal of MatEx is to produce parts for the end-use market that will experience practical use. The current state of the process prevents this due to a lack of robust mechanical properties and material versatility. The lack of versatility stems from a highly limited material catalog. MatEx is primarily commercially limited to polylactic acid (PLA) and acrylonitrile butadiene styrene (ABS). This small catalog of materials also limits the mechanical properties of the parts produced with this technique, as the mechanical properties of these two materials are much less than the common engineering thermoplastics such as polyetherimide (PEI) and polyether ether ketone (PEEK). The weakness of the produced parts also stems from a lack of z-axis, interlayer, adhesion. This problem is inherent to the process due to a low amount of time above the glass transition temperature of the material during printing, preventing the diffusion of polymer chains across the interface formed where two roads meet.

To address these issues, we propose a more thorough understanding of MatEx is required in order to more efficiently identify novel materials as well as their optimal printing conditions. Because in-situ measurements of the process are difficult, often resulting in unforeseen changes in the system that render any information gained with the measurements incorrect, we suggest utilizing multiphysics based process models to examine the effect of various process parameters on the quality of the final product. In this study, we developed multiple predictive models based on thermal transport, diffusion, and stress development. By examining the effect caused by modifying various parameters in these models, we can gain a detailed understanding of the process and how it can be modified to allow the printing of novel materials as well as identifying the optimal printing parameters for the production of parts with near bulk properties. These predictive models were then compared to empirical measurements.