Dimensional Accuracy of 3D Printed Dog-bone Tensile Samples: A Case Study

Authors

  • Dragos Gabriel Zisopol Mechanical Engineering Department, Petroleum-Gas University Ploiesti, Romania
  • Alexandra Ileana Portoaca Mechanical Engineering Department, Petroleum-Gas University Ploiesti, Romania
  • Maria Tanase Mechanical Engineering Department, Petroleum-Gas University Ploiesti, Romania
Volume: 13 | Issue: 4 | Pages: 11400-11405 | August 2023 | https://doi.org/10.48084/etasr.6060

Abstract

Three-dimensional (3D) printing technology has revolutionized manufacturing by enabling the rapid production of complex objects. However, ensuring dimensional accuracy in 3D printed parts remains a significant challenge due to various factors, including the selection of appropriate parameters during the Fused Deposition Modeling (FDM) process. Achieving dimensional accuracy is crucial in determining the reliability of a printing machine to produce objects that meet the expected results. This study aims to investigate the influence of FDM parameters (filling percentage and layer thickness) on the final dimensions of 3D printed parts made from polylactic acid (PLA) through a systematic experimental and statistical approach. The goal is to identify the optimal process parameter settings that minimize the error percentage in the dimensions of the printed parts using the Taguchi method. Overall higher dimensional accuracy was obtained, influenced mainly by the layer thickness parameter (in the case of Y direction dimensions) and by the filling percentage (in the case of Z direction dimensions – corresponding to sample thickness). The findings of this study provide valuable insight into identifying the optimal configuration for producing PLA 3D-printed components.

Keywords:

3D printing, FDM, PLA, dimensional accuracy, printing process parameters

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How to Cite

[1]
Zisopol, D.G., Portoaca, A.I. and Tanase, M. 2023. Dimensional Accuracy of 3D Printed Dog-bone Tensile Samples: A Case Study. Engineering, Technology & Applied Science Research. 13, 4 (Aug. 2023), 11400–11405. DOI:https://doi.org/10.48084/etasr.6060.

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