Optimization of FDM Process Parameters on Dimensional Error of ABS Parts by Response Surface Methodology

Authors

  • Tattapong Limlay Department of Mechanical Engineering Technology, College of Industrial Technology King Mongkut’s University of Technology North Bangkok
  • Wannalak Laotaweesub Department of Mechanical Engineering Technology, College of Industrial Technology King Mongkut’s University of Technology North Bangkok

Keywords:

additive manufacturing, layer thickness, printing speed, wall thickness, central composite design

Abstract

This research aims to determine optimal parameters for the Fused Deposition Modeling--FDM process to reduce the dimensional tolerance of ABS parts within the specified tolerance range. The parameters considered include layer thickness, print speed, and wall thickness. The experimental design was then performed using the Central Composite Design method, with response values collected by measuring the dimensions of the study parts using a Vernier caliper. The results from setting the printing process parameters are a layer thickness of 0.3485 mm, a printing speed of 20 mm/s, and a wall thickness of 2.4949 mm. After completing the print, the printer should be rested for at least 10 minutes. This results in the dimensions of the case study parts falling within the specified tolerance.

References

Aslani, K. E., Kitsakis, K., & Kechagias, J. D. (2020). On the application of grey Taguchi method for benchmarking the dimensional accuracy of the PLA fused filament fabrication process. Sciences, 2, 1016. https://doi.org/10.1007/s42452-020-2823-z

Buj-Corral, I., Bagheri, A., & Sivatte-Adroer, M. (2021). Effect of Printing Parameters on Dimensional Error, Surface Roughness and Porosity of FFF Printed Parts with Grid Structure. Polymer, 13, 1213. https://doi.org/10.3390/polym13081213

Buj-Corral, I., & Zayas-Figueras, E. E. (2023) “Comparative study about dimensional accuracy and form errors of FFF printed spur gears using PLA and Nylon. Polymer Testing, 117, 107862. https://doi.org/10.1016/j.polymertesting.2022.107862

Choudhari, C.M., & Patil, V. D. (2016) “Product Development and its Comparative Analysis by SLA, SLS and FDM Rapid Prototyping Processes. Materials Science and Engineering, 149, 012009. https://doi.org/10.1088/1757-899X/149/1/012009

Calì, J., Calian, D. A., Amati, C., & Kleinberger, R. (2012). 3D-Printing of Non - Assembly, Articulated Models. ACM Transactions on Graphics, 31(6), 130. https://doi.org/10.1145/2366145.236614

Dey, A., & Yodo, N. (2019) A Systematic Survey of FDM Process Parameter Optimization and Their Influence on Part Characteristics. Journal of Manufacturing and Materials Processing, 3, 64. https://doi.org/10.3390/jmmp3030064

Elkaseer, A., Schneider, S., & Scholz, S. G. (2020). Experiment-Based Process Modeling and Optimization for High-Quality and Resource-Efficient FFF 3D Printing. Sciences, 10, 2899. https://doi.org/10.3390/app10082899

Equbal, A., Soon, A. K.., & Razzaq, A. (2017). Optimization of process parameters of FDM part for minimizing its dimensional inaccuracy. International Journal of Mechanical and Production, 7(2), 57-66. https://bit.ly/42bclXc

Hanon, M. M., Zsidai, L., & Ma, Q. (2021). Accuracy investigation of 3D printed PLA with various process parameters and different colors. Materials Today: Proceedings, 42, 3089-3096. https://doi.org/10.1016/j.matpr.2020.12.1246

Gautam, J., & Aravinth, N. (2022). Studies on the effect of part geometry and process parameter on the dimensional deviation of additive manufactured part using ABS material. Progress in Additive Manufacturing. Progress in Additive Manufacturing, 6, 1183-1193. https://doi.org/10.1007/s40964-022-00292-9

Gao, G., & Xu, F. (2022). Parametric optimization of FDM process for improving mechanical strengths using Taguchi method and response surface method: A comparative investigation. Machines, 10, 750. https://doi.org/10.3390/machines10090750

Qureshi, S. M., & Talamona, D. (2018). Taguchi based process optimization for dimension and tolerance control for fused deposition modelling. Additive Manufacturing, 21, 183-190. https://doi.org/10.1016/j.addma.2018.03.009

Raymond, M. (2009) Experimental Designs for Fitting Response Surfaces – I. In W. A. Shewhart & S. S. Wilks (Eds.), Response Surface Methodology (pp. 281-348). United States: Wiley

Shakeri, Z., Benfriha, K., & Zirak, N. (2021). Optimization of FFF Processing Parameters to Improve Geometrical Accuracy and Mechanical Behavior of Polyamide 6 Using Grey Relational Analysis (GRA). ISSI Scientific Reports Series, 2021, 1-29. https://doi.org/10.21203/rs.3.rs-1118150/v1

Sieminski, P. (2021). Chapter 2 - Introduction to fused deposition modeling. In J. Pou, A. Riveiro & J. P. Davim (Eds), Additive manufacturing (pp. 217-275). Netherlands: Elsevier

Solomon, J., & Gunasekaran, S. (2020). A review on the various processing parametersin FDM. Materials Today: Proceedings, 37(2), 509-514. https://doi.org/10.1016/j.matpr.2020.05.484

Sood, A. K., & Mahapatra, O. (2009). Improving dimensional accuracy of Fused Deposition Modelling processed part using grey Taguchi method. Materials and Design, 30, 4243–4252. https://doi.org/10.1016/j.matdes.2009.04.030

Sudin, M. N., Shamsudin, S. A., & Abdullah, M. A. (2016). Effect of part features on dimensional accuracy of FDM model. ARPN Journal of Engineering and Applied Sciences, 11(13), 8067-8072. https://www.arpnjournals.org/jeas/research_papers/rp_2016/jeas_0716_4564.pdf

Suthat Na Ayutthaya, P., & Luangpaiboon, P. (2551). Design and analysis of experiments. Bangkok: Top publishing house (in Thai)

Vanaei, H. R., Khellad, S., & Tcharkhtchi, A. (2022). Roadmap: Numerical-experimental investigation and optimization of 3D-printed parts using response surface methodology. Materials, 15, 7193. https://doi.org/10.3390/ma15207193

Vijayakumar, S., & Ayyangar, A. K. (2018) Design and fabrication of industrial components using 3D printing. Materials Today: Proceedings, 5, 14489–14498. https://doi.org/10.1016/j.matpr.2018.03.036

Wicaksono, M. B., & Nugraha, F. A. N. (2022) “Optimization of 3D printing parameters using the Taguchi method to improve dimensional precision. Additive Manufacturing, 12(2), 70-75. https://doi.org/10.35134/jitekin.v12i2.72

Wu, J. (2018) Study on optimization of 3D printing parameters. Materials Science and Engineering, 392, 062050. https://doi.org/10.1088/1757-899X/392/6/062050

Yan, Q., Dong, H., Su, J., Han, J., Song, B., Wei, Q, & Shi, Y. (2018). A review of 3D printing technology for medical applications. Engineering, 4, 729–742. https://doi.org/10.1016/j.eng.2018.07.021

Zadpoor, A. A., & Malda, J. (2016). Additive Manufacturing of Biomaterials, Tissues and Organs. Biomedical Engineering, 45, 1-11. https://doi.org/10.1007/s10439-016-1719-y.

Zhang, X., & Liou, F. (2021) Chapter 1 - Introduction to additive manufacturing. In J. Pou, A. Riveiro, & J. P. Davim (Eds), Additive manufacturing (pp. 1-31). Netherlands: Elsevier

Downloads

Published

2025-04-21

How to Cite

Limlay, T. ., & Laotaweesub, W. . (2025). Optimization of FDM Process Parameters on Dimensional Error of ABS Parts by Response Surface Methodology. EAU Heritage Journal Science and Technology (Online), 19(1), 98–115. retrieved from https://he01.tci-thaijo.org/index.php/EAUHJSci/article/view/272429

Issue

Section

Research Articles