A Grey Relational Analysis Approach to Optimize FDM 3D Printing Parameters Using Open-Source Experimental Data

Sunardi Sunardi

doi:10.55927/fjst.v4i4.61

Authors

Sunardi Sunardi Universitas Muhammadiyah Yogyakarta

DOI:

https://doi.org/10.55927/fjst.v4i4.61

Keywords:

Fused Deposition Modeling, 3D Printing, Process Optimization, Gray Relational Analysis, Mechanical Properties

Abstract

This study investigates the optimization of key process parameters in Fused Deposition Modeling (FDM) to enhance surface quality and mechanical properties. Using open-source data from 50 test runs, nine parameters were analyzed. Gray Relational Analysis (GRA) identified the optimal setting—layer height (0.02 mm), wall thickness (9 mm), 70% infill density, grid pattern, 215 °C nozzle, 75 °C bed, 40 mm/s speed, PLA material, and 75% fan speed—with a Gray Relational Grade of 0.8685. This configuration significantly improved surface roughness, tensile strength, and elongation. The findings provide a practical reference for improving FDM performance. Future research should explore advanced materials and multi-objective optimization for broader applicability.

References

Chakraborty, S., Datta, H. N., & Chakraborty, S. (2023). Grey relational analysis-based optimization of machining processes: a comprehensive review. Process Integration and Optimization for Sustainability, 7(4), 609-639.

Chen, X. (2023). Overview of Gray System Theory. In Application of Gray System Theory in Fishery Science (pp. 1-20). Springer.

Dev, S., & Srivastava, R. (2021). Effect of infill parameters on material sustainability and mechanical properties in fused deposition modelling process: a case study. Progress in Additive Manufacturing, 6(4), 631-642.

Goh, G. D., Dikshit, V., Nagalingam, A. P., Goh, G. L., Agarwala, S., Sing, S. L., Wei, J., & Yeong, W. Y. (2018). Characterization of mechanical properties and fracture mode of additively manufactured carbon fiber and glass fiber reinforced thermoplastics. Materials & Design, 137, 79-89.

Gordelier, T. J., Thies, P. R., Turner, L., & Johanning, L. (2019). Optimising the FDM additive manufacturing process to achieve maximum tensile strength: a state-of-the-art review. Rapid Prototyping Journal, 25(6), 953-971.

Kechagias, J., & Zaoutsos, S. (2024). Effects of 3D-printing processing parameters on FFF parts’ porosity: Outlook and trends. Materials and Manufacturing Processes, 39(6), 804-814.

Khan, S., Joshi, K., & Deshmukh, S. (2022). A comprehensive review on effect of printing parameters on mechanical properties of FDM printed parts. Materials Today: Proceedings, 50, 2119-2127.

Kumar, P., Gupta, P., & Singh, I. (2024). Optimisation of extrusion process parameters to make ABS-PC filament for 3D printing using Taguchi-GRA technique. Advances in Materials and Processing Technologies, 10(3), 1921-1942.

Kumar, S., Singh, R., Singh, T., & Batish, A. (2023). Fused filament fabrication: A comprehensive review. Journal of Thermoplastic Composite Materials, 36(2), 794-814.

Lee, S., Hwang, B., Lim, S., Yoon, S., Kim, T., Kim, K., Kim, D., & Park, C. (2019). Recent Trends and Prospects of 3D Content Using Artificial Intelligence Technology. Electronics and Telecommunications Trends, 34(4), 15-22.

Molero, E., Fernández, J. J., Rodríguez-Alabanda, O., Guerrero-Vaca, G., & Romero, P. E. (2020). Use of data mining techniques for the prediction of surface roughness of printed parts in polylactic acid (PLA) by fused deposition modeling (FDM): A practical application in frame glasses manufacturing. Polymers, 12(4), 840.

Mourya, V., Bhore, S. P., & Wandale, P. G. (2024). Multiobjective optimization of tribological characteristics of 3D printed texture surfaces for ABS and PLA Polymers. Journal of Thermoplastic Composite Materials, 37(2), 772-799.

Nazari, F., & Abedi, A. (2025). Investigating the effect of perforation geometry on the residual stress and mechanical behavior of 3D-printed honeycomb structure. Multidiscipline Modeling in Materials and Structures.

Patel, K., Acharya, S., & Acharya, G. (2024). Multi objective optimization of FDM parameters using taguchi grey relation analysis for PLA specimen. Jurnal Kejuruteraan, 36(1), 113-122.

Raju, R., Varma, M. M. M., & Baghel, P. K. (2022). Optimization of process parameters for 3D printing process using Taguchi based grey approach. Materials Today: Proceedings, 68, 1515-1520.

Shanmugam, V., Babu, K., Kannan, G., Mensah, R. A., Samantaray, S. K., & Das, O. (2024). The thermal properties of FDM printed polymeric materials: A review. Polymer Degradation and Stability, 110902.

Tamir, T. S., Xiong, G., Fang, Q., Yang, Y., Shen, Z., Zhou, M., & Jiang, J. (2023). Machine-learning-based monitoring and optimization of processing parameters in 3D printing. International Journal of Computer Integrated Manufacturing, 36(9), 1362-1378.

Vanaei, S., & Elahinia, M. (2024). Applicable Materials and Techniques in 3D Printing. Industrial Strategies and Solutions for 3D Printing: Applications and Optimization, 43-57.

Xia, Y., Qian, S., Lu, W., Zhang, Z., Cheng, H., & Sheng, K. (2023). A strategy to prepare high‐robust and ultra‐tough polylactic acid/polycaprolactone/talc composites with thermo‐resistance. Polymer Composites, 44(12), 8750-8765.

Yi, N., Chen, Y., Shen, J., Davies, R., & Ghita, O. (2024). Correlation between interfacial bond strength and degree of healing in overprinting PAEK on CF/PAEK composites. Composites Part A: Applied Science and Manufacturing, 183, 108217.

Zeng, S., You, G., Yao, F., Li, Q., Wang, L., & Cao, H. (2022). Coupling effect of bonding temperature and reduced interlayer thickness on the interface characteristics and quality of the diffusion-bonded joints of Zr alloys. Journal of Materials Research and Technology, 18, 2699-2710.