A Multi-criteria decision making approach for 3D printer nozzle material selection


  • Saikat Chatterjee Department of Mechanical Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Sikkim, India
  • Shankar Chakraborty Department of Production Engineering, Jadavpur University, Kolkata, India




3D printing, Nozzle material, Entropy, EDAS, Sensitivity analysis


Rapid advancements in 3D printing technology have compelled the manufacturers to search for better nozzle material in the extruder of 3D printers. Materials ranging from brass to tungsten carbide and ruby are primarily used as the nozzle material. In 3D printing technology, due to major constraints imposed by the filament material and other decisive factors, no single nozzle material satisfies all the desired characteristics for a real time application. Thus, it has become crucial to select the most appropriate nozzle material with the desired properties for enhanced 3D printing performance. In this paper, the performance of eight candidate nozzle materials is evaluated based on nine selection criteria. Entropy method is utilized to determine the criteria weights, whereas, evaluation based on distance from average solution (EDAS) method is employed to identify the best suited 3D printer nozzle material. Tungsten carbide emerges out as the best choice, followed by titanium alloy (TiAl6V4). This paper also proposes a sensitivity analysis to establish the robustness of the adopted methodology.


AL-Oqla, F. M., Sapuan, S. M., Ishak, M. R., & Nuraini, A. A. (2016). A decision-making model for selecting the most appropriate natural fiber – Polypropylene-based composites for automotive applications. Journal of Composite Materials, 50(4), 543–556. https://doi.org/10.1177/0021998315577233

Anojkumar, L., Ilangkumaran, M., & Hassan, S. M. (2016). An integrated hybrid multi-criteria decision making technique for material selection in the sugar industry. International Journal of Multicriteria Decision Making, 6(3), 247. https://doi.org/10.1504/IJMCDM.2016.079719

Bhattacharyya, O., & Chakraborty, S. (2015). Q-analysis in Materials Selection. Decision Science Letters, 51–62. https://doi.org/10.5267/j.dsl.2014.9.001

Carolo, L. (2022, January 30). The best 3D printer nozzle types, sizes & materials. https://all3dp.com/2/3d-printer-nozzle-size-material-what-to-know-which-to-buy/

Chakraborty, S., & Chatterjee, P. (2013). Selection of materials using multi-criteria decision-making methods with minimum data. Decision Science Letters, 2(3), 135–148. https://doi.org/10.5267/j.dsl.2013.03.005

Chang, D.-Y. (1996). EUROPEAN JOURNAL OF OPERATIONAL RESEARCH Applications of the extent analysis method on fuzzy AHP. In European Journal of Operational Research (Vol. 95).

CHATTERJEE, P., BANERJEE, A., MONDAL, S., BORAL, S., & CHAKRABORTY, S. (2018). Development of a Hybrid Meta-Model for Material Selection Using Design of Experiments and EDAS Method. Engineering Transactions, 66(2). https://et.ippt.gov.pl/index.php/et/article/view/812

Chatterjee, P., Mandal, N., Dhar, S., Chatterjee, S., & Chakraborty, S. (2020). A novel decision-making approach for light weight environment friendly material selection. Materials Today: Proceedings, 22, 1460–1469. https://doi.org/https://doi.org/10.1016/j.matpr.2020.01.504

Chatterjee, S., & Chakraborty, S. (2022). A multi-attributive ideal-real comparative analysis-based approach for piston material selection. OPSEARCH, 59(1), 207–228. https://doi.org/10.1007/s12597-021-00536-2

Chede, S., Keswani, M., Patil, A., Adavadkar, B., & Chhatriwala, H. (2020). Material selection for design of powered hand truck using TOPSIS. International Journal of Industrial and Systems Engineering, 1(1), 1. https://doi.org/10.1504/IJISE.2020.10028965

Dev, S., Aherwar, A., & Patnaik, A. (2020). Material Selection for Automotive Piston Component Using Entropy-VIKOR Method. Silicon, 12(1), 155–169. https://doi.org/10.1007/s12633-019-00110-y

Ghaleb, A. M., Kaid, H., Alsamhan, A., Mian, S. H., & Hidri, L. (2020). Assessment and Comparison of Various MCDM Approaches in the Selection of Manufacturing Process. Advances in Materials Science and Engineering, 2020. https://doi.org/10.1155/2020/4039253

Ghorabaee, M. K., Zavadskas, E. K., Olfat, L., & Turskis, Z. (2015). Multi-Criteria Inventory Classification Using a New Method of Evaluation Based on Distance from Average Solution (EDAS). Informatica (Netherlands), 26(3), 435–451. https://doi.org/10.15388/Informatica.2015.57

Giorgetti, A., Cavallini, C., Arcidiacono, G., & Citti, P. (2017). A Mixed C-VIKOR Fuzzy Approach for Material Selection during Design Phase: A Case Study in Valve Seats for High Performance Engine. In International Journal of Applied Engineering Research (Vol. 12). http://www.ripublication.com

Goswami, S. S., & Behera, D. K. (2020). Implementation of ENTROPY-ARAS decision making methodology in the selection of best engineering materials. Materials Today: Proceedings, 38, 2256–2262. https://doi.org/10.1016/j.matpr.2020.06.320

Gul, M., Celik, E., Gumus, A. T., & Guneri, A. F. (2018). A fuzzy logic based PROMETHEE method for material selection problems. Beni-Suef University Journal of Basic and Applied Sciences, 7(1), 68–79. https://doi.org/10.1016/j.bjbas.2017.07.002

Hasanzadeh, R., Azdast, T., Lee, R. E., & Ghazi, A. (2017). Experimental polymeric nanocomposite material selection for automotive bumper beam using multi-criteria decision making methods. Iranian Journal of Materials Science and Engineering, 14, 1–10. https://doi.org/10.22068/ijmse.l4.3.1

Ishak, N. M., Malingam, S. D., & Mansor, M. R. (2016). Selection of natural fibre reinforced composites using fuzzy VIKOR for car front hood. International Journal of Materials and Product Technology, 53(3–4), 267–285. https://doi.org/10.1504/IJMPT.2016.079205

Jayakrishna, K., & Vinodh, S. (2017). Application of grey relational analysis for material and end of life strategy selection with multiple criteria. In Int. J. Materials Engineering Innovation (Vol. 8).

Karaşan, A., Kahraman, C., & Boltürk, E. (2019). Interval-valued neutrosophic EDAS method: An application to prioritization of social responsibility projects. In Studies in Fuzziness and Soft Computing (Vol. 369, pp. 455–485). Springer Verlag. https://doi.org/10.1007/978-3-030-00045-5_18

Keshavarz-Ghorabaee, M., Amiri, M., Zavadskas, E. K., Turskis, Z., & Antucheviciene, J. (2018). A comparative analysis of the rank reversal phenomenon in the EDAS and TOPSIS methods. Economic Computation and Economic Cybernetics Studies and Research, 52(3), 121–134. https://doi.org/10.24818/18423264/

Khandekar, A. V, & Chakraborty, S. (2015). Decision-making for materials selection using fuzzy axiomatic design principles. In Int. J. Industrial and Systems Engineering (Vol. 20, Issue 1).

Kumar, R., & Singal, S. K. (2015). Penstock material selection in small hydropower plants using MADM methods. In Renewable and Sustainable Energy Reviews (Vol. 52, pp. 240–255). Elsevier Ltd. https://doi.org/10.1016/j.rser.2015.07.018

Kundakcı, N. (2019). An integrated method using MACBETH and EDAS methods for evaluating steam boiler alternatives. Journal of Multi-Criteria Decision Analysis, 26(1–2), 27–34. https://doi.org/10.1002/mcda.1656

Lima Junior, F. R., Osiro, L., & Carpinetti, L. C. R. (2014). A comparison between Fuzzy AHP and Fuzzy TOPSIS methods to supplier selection. Applied Soft Computing Journal, 21, 194–209. https://doi.org/10.1016/j.asoc.2014.03.014

Lu, B., Li, D., & Tian, X. (2015). Development Trends in Additive Manufacturing and 3D Printing. Engineering, 1(1), 085–089. https://doi.org/10.15302/J-ENG-2015012

Maity, S. R., & Chakraborty, S. (2015). Tool steel material selection using PROMETHEE II method. International Journal of Advanced Manufacturing Technology, 78(9–12), 1537–1547. https://doi.org/10.1007/s00170-014-6760-0

MatWeb - Online Materials Information Resource. Automation Creations. (2023, January 22). https://www.matweb.com/

Nozzle Sizes, Materials, and Shapes for 3D Printers. (2017, December 14). https://www.fargo3dprinting.com/nozzle-sizes-materials-shapes-3d-printers/

Rastogi, M., Chauhan, A., Vaish, R., & Kishan, A. (2015). Selection and performance assessment of Phase Change Materials for heating, ventilation and air-conditioning applications. Energy Conversion and Management, 89, 260–269. https://doi.org/10.1016/j.enconman.2014.09.077

Saputra, R. S. H., Iskandar, B. H., Kurniawati, V. R., Desrial, D., & Purbayanto, A. (2023). Material Selection of Collapsible Pot Hauler and Finite Element Analysis Simulation Applied to the Selected Material. Advances in Science and Technology. Research Journal, 17(2), 289–298.

Schitea, D., Deveci, M., Iordache, M., Bilgili, K., Akyurt, İ. Z., & Iordache, I. (2019). Hydrogen mobility roll-up site selection using intuitionistic fuzzy sets based WASPAS, COPRAS and EDAS. International Journal of Hydrogen Energy, 44(16), 8585–8600. https://doi.org/10.1016/j.ijhydene.2019.02.011

Sen, B., Bhattacharjee, P., & Mandal, U. K. (2016). A comparative study of some prominent multi criteria decision making methods for connecting rod material selection. Perspectives in Science, 8, 547–549. https://doi.org/10.1016/j.pisc.2016.06.016

Shokr., I; Torabi, S. A. (2015). A Common Weight Data Envelopment Analysis Approach for Material Selection. International Journal of Engineering, 28(6 (C)). https://doi.org/10.5829/idosi.ije.2015.28.06c.12

Shugurov, A. R., Nikonov, A. Y., & Dmitriev, A. I. (2022). The effect of electron-beam treatment on the deformation behavior of the ebam TI-6AL-4V under scratching. Facta Universitatis, Series: Mechanical Engineering, 20(2), 307–319. https://doi.org/10.22190/FUME211110001S

Singh, T., Patnaik, A., Gangil, B., & Chauhan, R. (2015). Optimization of tribo-performance of brake friction materials: Effect of nano filler. Wear, 324–325, 10–16. https://doi.org/10.1016/j.wear.2014.11.020

Stević, Ž., Vasiljević, M., Puška, A., Tanackov, I., Junevičius, R., & Vesković, S. (2019). Evaluation of suppliers under uncertainty: a multiphase approach based on Fuzzy AHP and Fuzzy EDAS. Transport, 34(1), 52–66. https://doi.org/10.3846/transport.2019.7275

Xia, H., Lu, J., & Tryggvason, G. (2017). Fully Resolved Numerical Simulations of Fused Deposition Modeling. Part II-Solidification, Residual Stresses, and Modeling of the Nozzle. http://arxiv.org/abs/1711.07094

Xue, Y. X., You, J. X., Lai, X. D., & Liu, H. C. (2016). An interval-valued intuitionistic fuzzy MABAC approach for material selection with incomplete weight information. Applied Soft Computing Journal, 38, 703–713. https://doi.org/10.1016/j.asoc.2015.10.010

Yadav, S., Pathak, V. K., & Gangwar, S. (2019). A novel hybrid TOPSIS-PSI approach for material selection in marine applications. Sādhanā, 44(3), 58. https://doi.org/10.1007/s12046-018-1020-x

Yang, S. S., Nasr, N., Ong, S. K., & Nee, A. Y. C. (2017). Designing automotive products for remanufacturing from material selection perspective. Journal of Cleaner Production, 153, 570–579. https://doi.org/10.1016/j.jclepro.2015.08.121

Yazdani, M., Torkayesh, A. E., & Chatterjee, P. (2020). An integrated decision-making model for supplier evaluation in public healthcare system: the case study of a Spanish hospital. Journal of Enterprise Information Management, 33(5), 965–989. https://doi.org/10.1108/JEIM-09-2019-0294

Zindani, D., Maity, S. R., Bhowmik, S., & Chakraborty, S. (2017). A material selection approach using the TODIM (TOmada de Decisao Interativa Multicriterio) method and its analysis. 108(5), 345–354. https://doi.org/doi:10.3139/146.111489

Zou, Z., Yun, Y., & Sun, J. (2006). Entropy method for determination of weight of evaluating indicators in fuzzy synthetic evaluation for water quality assessment. Journal of Environmental Sciences, 18(5), 1020–1023. https://doi.org/https://doi.org/10.1016/S1001-0742(06)60032-6



How to Cite

Chatterjee, S. ., & Chakraborty, S. . (2023). A Multi-criteria decision making approach for 3D printer nozzle material selection. Reports in Mechanical Engineering, 4(1), 62–79. https://doi.org/10.31181/rme040121042023c