On the Heuristic Procedure to Determine Processing Parameters in Additive Manufacturing based on Materials Extrusion
Date
2020
Authors
Bakradze, Georgijs
Arājs, Egīls
Gaidukovs, Sergejs
Thakur, Vijay Kumar
Journal Title
Journal ISSN
Volume Title
Publisher
MDPI AG
Abstract
We present a heuristic procedure for determining key processing parameters (PPs) in materials-extrusion-based additive manufacturing processes. The concept relies on a design-of-experiment approach and consists of eleven “test objects” to determine the optimal combinations of key PPs values, starting with the PPs for printing the first layer and progressing to more complex geometric features, e.g., “bridges”. In each of the test objects, several combinations of the known PPs’ values are used, and only the values resulting in the best printed-part quality are selected for the following tests. The concept is intrinsically insensitive to different artefacts of the additive manufacturing machine (e.g., discrepancies between the nominal and actual nozzle diameters, and improper calibration of the feeding screws) and the optimal values of key PPs for manufacturing defect-free parts under the actual processing conditions can be determined. We validated the proposed procedure for two common commercial polymer feedstock materials, and we show that, by using the proposed procedure, it is possible to reduce the optimization time down to several hours, as well as to reduce the amount of consumed feedstock material. Tensile tests revealed a strong effect of amorphous and semi-crystalline nature of the polymer on the results of optimization. To the best of our knowledge, this is the first attempt to describe a systematic approach for optimizing PPs for materials extrusion-based additive manufacturing processes without relying on statistical data analysis or virtual simulations. The concept was implemented as a web-tool 3DOptimizer®.
Description
Keywords
Research Subject Categories::NATURAL SCIENCES:Physics , 3D printing , FDM , materials extrusion , process parameters , optimization , design of experiment