TY - GEN
T1 - Development of Correction Factors for FDM 3D Printers
T2 - 8th International Conference on Sustainable Design and Manufacturing, KES-SDM 2021
AU - Müller, Tobias
AU - Elkaseer, Ahmed
AU - Wadlinger, Janik
AU - Salem, Mahmoud
AU - Scholz, Steffen G.
N1 - Funding Information:
Acknowledgement. This work was carried out with the support of the Karlsruhe Nano Micro Facility (KNMFi, www.knmf.kit.edu) a Helmholtz Research Infrastructure at Karlsruhe Institute of Technology (KIT, www.kit.edu) and under the Helmholtz Research Programme MSE (Material Systems Engineering) at KIT.
Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2022
Y1 - 2022
N2 - While additive manufacturing is already applied in industry in different varieties, even simple and widespread techniques such as the fused filament fabrication (FFF) process are still not fully understood in terms of influence of printing parameters or part orientation on the quality of fabricated parts. A main issue for using FFF in a reliable manner is the dimensional control of printed parts in comparison to the initially planned model. In this study the influence of printing parameters, part positioning on the printing platform as well as the use of different printer models has been evaluated in order to find correction factors for an optimised FFF process for high precision parts. The investigation has been carried out with two common printing materials (ABS and PLA) and printing tests have been conducted on two different FFF printers to evaluate the influence of hardware differences. Examination of the gathered data showed a substantial scattering of measured results, making the application of correction factors difficult, but not impossible. In addition, to predict the outcome of future prints a modelling approach using an artificial neural network (ANN) algorithm is presented. The developed ANN model paves the way for identifying a processing window, 3D printing parameters and correction factors for an optimised FFF process for high precision printed parts.
AB - While additive manufacturing is already applied in industry in different varieties, even simple and widespread techniques such as the fused filament fabrication (FFF) process are still not fully understood in terms of influence of printing parameters or part orientation on the quality of fabricated parts. A main issue for using FFF in a reliable manner is the dimensional control of printed parts in comparison to the initially planned model. In this study the influence of printing parameters, part positioning on the printing platform as well as the use of different printer models has been evaluated in order to find correction factors for an optimised FFF process for high precision parts. The investigation has been carried out with two common printing materials (ABS and PLA) and printing tests have been conducted on two different FFF printers to evaluate the influence of hardware differences. Examination of the gathered data showed a substantial scattering of measured results, making the application of correction factors difficult, but not impossible. In addition, to predict the outcome of future prints a modelling approach using an artificial neural network (ANN) algorithm is presented. The developed ANN model paves the way for identifying a processing window, 3D printing parameters and correction factors for an optimised FFF process for high precision printed parts.
KW - Additive manufacturing
KW - ANN modelling
KW - Dimensional accuracy
KW - Parameter optimisation
KW - Process control
UR - https://www.scopus.com/pages/publications/85115832596
U2 - 10.1007/978-981-16-6128-0_30
DO - 10.1007/978-981-16-6128-0_30
M3 - Conference contribution
AN - SCOPUS:85115832596
SN - 9789811661273
T3 - Smart Innovation, Systems and Technologies
SP - 314
EP - 326
BT - Sustainable Design and Manufacturing - Proceedings of the 8th International Conference on Sustainable Design and Manufacturing, KES-SDM 2021
A2 - Scholz, Steffen G.
A2 - Howlett, Robert J.
A2 - Setchi, Rossi
PB - Springer
Y2 - 15 September 2021 through 17 September 2021
ER -