On the Heuristic Procedure to Determine Processing Parameters in Additive Manufacturing Based on Materials Extrusion

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, startin...

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Main Authors: Georgijs Bakradze, Egīls Arājs, Sergejs Gaidukovs, Vijay Kumar Thakur
Format: Article
Language:English
Published: MDPI AG 2020-12-01
Series:Polymers
Subjects:
Online Access:https://www.mdpi.com/2073-4360/12/12/3009
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author Georgijs Bakradze
Egīls Arājs
Sergejs Gaidukovs
Vijay Kumar Thakur
author_facet Georgijs Bakradze
Egīls Arājs
Sergejs Gaidukovs
Vijay Kumar Thakur
author_sort Georgijs Bakradze
collection DOAJ
description 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<sup>®</sup>.
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spelling doaj.art-d59ae21aaf134513a8799df7551e8a9e2023-11-21T01:07:20ZengMDPI AGPolymers2073-43602020-12-011212300910.3390/polym12123009On the Heuristic Procedure to Determine Processing Parameters in Additive Manufacturing Based on Materials ExtrusionGeorgijs Bakradze0Egīls Arājs1Sergejs Gaidukovs2Vijay Kumar Thakur3Institute of Solid State Physics, University of Latvia, LV-1063 Riga, LatviaFabControl Ltd., LV-1007 Riga, LatviaInstitute of Polymer Materials Faculty of Materials Science and Applied Chemistry, Riga Technical University, LV-1048 Riga, LatviaBiorefining and Advanced Materials Research Center, Scotland’s Rural College (SRUC), Kings Buildings, Edinburgh EH9 3JG, UKWe 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<sup>®</sup>.https://www.mdpi.com/2073-4360/12/12/30093D printingFDMmaterials extrusionprocess parametersoptimizationdesign of experiment
spellingShingle Georgijs Bakradze
Egīls Arājs
Sergejs Gaidukovs
Vijay Kumar Thakur
On the Heuristic Procedure to Determine Processing Parameters in Additive Manufacturing Based on Materials Extrusion
Polymers
3D printing
FDM
materials extrusion
process parameters
optimization
design of experiment
title On the Heuristic Procedure to Determine Processing Parameters in Additive Manufacturing Based on Materials Extrusion
title_full On the Heuristic Procedure to Determine Processing Parameters in Additive Manufacturing Based on Materials Extrusion
title_fullStr On the Heuristic Procedure to Determine Processing Parameters in Additive Manufacturing Based on Materials Extrusion
title_full_unstemmed On the Heuristic Procedure to Determine Processing Parameters in Additive Manufacturing Based on Materials Extrusion
title_short On the Heuristic Procedure to Determine Processing Parameters in Additive Manufacturing Based on Materials Extrusion
title_sort on the heuristic procedure to determine processing parameters in additive manufacturing based on materials extrusion
topic 3D printing
FDM
materials extrusion
process parameters
optimization
design of experiment
url https://www.mdpi.com/2073-4360/12/12/3009
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