Modeling of the influence of input AM parameters on dimensional error and form errors in PLA parts printed with FFF technology
| dc.contributor.author | Luis Pérez, Carmelo Javier | |
| dc.contributor.author | Buj Corral, Irene | |
| dc.contributor.author | Sánchez Casas, Xabier | |
| dc.contributor.department | Ingeniería | es_ES |
| dc.contributor.department | Ingeniaritza | eu |
| dc.date.accessioned | 2022-04-12T08:44:49Z | |
| dc.date.available | 2022-04-12T08:44:49Z | |
| dc.date.issued | 2021 | |
| dc.description.abstract | As is widely known, additive manufacturing (AM) allows very complex parts to be manufactured with porous structures at a relatively low cost and in relatively low manufacturing times. However, it is necessary to determine in a precise way the input values that allow better results to be obtained in terms of microgeometry, form errors, and dimensional error. In an earlier work, the influence of the process parameters on surface roughness obtained in fused filament fabrication (FFF) processes was analyzed. This present study focuses on form errors as well as on dimensional error of hemispherical cups, with a similar shape to that of the acetabular cup of hip prostheses. The specimens were 3D printed in polylactic acid (PLA). Process variables are nozzle diameter, temperature, layer height, print speed, and extrusion multiplier. Their influence on roundness, concentricity, and dimensional error is considered. To do this, adaptive neuro-fuzzy inference systems (ANFIS) models were used. It was observed that dimensional error, roundness, and concentricity depend mainly on the nozzle diameter and on layer height. Moreover, high nozzle diameter of 0.6 mm and high layer height of 0.3 mm are not recommended. A desirability function was employed along with the ANFIS models in order to determine the optimal manufacturing conditions. The main aim of the multi-objective optimization study was to minimize average surface roughness (Ra) and roundness, while dimensional error was kept within the interval. When the simultaneous optimization of both the internal and the external surface of the parts is performed, it is recommended that a nozzle diameter of 0.4 mm be used, to have a temperature of 197 °C, a layer height of 0.1 mm, a print speed of 42 mm/s, and extrusion multiplier of 94.8%. This study will help to determine the influence of the process parameters on the quality of the manufactured parts. | en |
| dc.description.sponsorship | This research was financed by the Spanish Ministry of Industry, Economy and Competitiveness, grant number PID2020-115647RB-C21. | en |
| dc.format.extent | 23 p. | |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.doi | 10.3390/polym13234152 | |
| dc.identifier.issn | 2073-4360 | |
| dc.identifier.uri | https://academica-e.unavarra.es/handle/2454/42735 | |
| dc.language.iso | eng | en |
| dc.publisher | MDPI | en |
| dc.relation.ispartof | Polymers, 2021, 13, 4152 | en |
| dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-115647RB-C21/ES/ | |
| dc.relation.publisherversion | https://doi.org/10.3390/polym13234152 | |
| dc.rights | © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. | en |
| dc.rights.accessRights | info:eu-repo/semantics/openAccess | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | Additive manufacturing | en |
| dc.subject | ANFIS | en |
| dc.subject | Desirability | en |
| dc.subject | FFF | en |
| dc.subject | Modeling | en |
| dc.subject | Surface roughness | en |
| dc.title | Modeling of the influence of input AM parameters on dimensional error and form errors in PLA parts printed with FFF technology | en |
| dc.type | info:eu-repo/semantics/article | |
| dc.type.version | info:eu-repo/semantics/publishedVersion | |
| dspace.entity.type | Publication | |
| relation.isAuthorOfPublication | f3b1f886-f01b-4881-9dbc-709b62a3f866 | |
| relation.isAuthorOfPublication.latestForDiscovery | f3b1f886-f01b-4881-9dbc-709b62a3f866 |