i-manager Publications

Multi Objective Optimization of 3D Printing Process using Multi-Attribute Decision Making Methods

V. Chengal Reddy *, S. Kavitha**, T. Nishkala***, S. M. Jameel Basha****

*-**** Department of Mechanical Engineering, Chadalawada Ramanamma Engineering College, Tirupati, Andhra Pradesh, India.

Periodicity:February - April'2022
DOI : https://doi.org/10.26634/jme.12.2.18562

Abstract

Fused deposition modeling (FDM) is one of the additive manufacturing (AM) methods widely used in many divisions, especially medical implants and aerospace, due to capabilities to build complex 3D objects and geometries. However, quality and dimensional accuracy of the FDM parts are significantly influenced by the various FDM process parameters including filament wire material. In the present work, new filament wire material Thermoplastic Polyurethane (TPU) was utilized to produce FDM parts. Hence, deciding the optimum process parameters is very critical to produce the FDM parts with good surface quality (Ra) and dimensional accuracy (Δd) concurrently using TPU material. In this paper, the author has contributed to determine the optimum 3D printing process parameters to improve the quality and accuracy for the new filament wire material Thermoplastic Polyurethane (TPU) using multi-attribute decision making (MADM) methods namely Gray Relational Analysis (GRA) and technique for order preference by similarity to ideal solution (TOPSIS). Further, the results of GRA and TOPSIS techniques were compared and concluded that TOPSIS method substantially reduced the surface roughness to a value of 12% contrast to the GRA method whereas the dimensional deviation accuracy increased to 6.25% over the GRA method.

Keywords

Dimensional Accuracy, Surface Roughness, MADM, GRA, TOPSIS, FDM, 3D Printing, Optimization.

How to Cite this Article?

Reddy, V. C., Kavitha, S., Nishkala, T., and Basha, S. M. J. (2022). Multi Objective Optimization of 3D Printing Process using Multi-Attribute Decision Making Methods. i-manager’s Journal on Mechanical Engineering, 12(2), 40-49. https://doi.org/10.26634/jme.12.2.18562

References

[1]. Boesch, E., Siadat, A., Rivette, M., & Baqai, A. A. (2019). Impact of fused deposition modeling (FDM) process parameters on strength of built parts using Taguchi's design of experiments. The International Journal of Advanced Manufacturing Technology, 101(5), 1215-1226. https://doi.org/10.1007/s00170-018-3014-6

[2]. Boparai, K. S., Singh, R., & Singh, H. (2016). Modeling and optimization of extrusion process parameters for the development of Nylon6–Al–Al2O3 alternative FDM filament. Progress in Additive Manufacturing, 1(1), 115-128. https://doi.org/10.1007/s40964-016-0011-x

[3]. Carlier, E., Marquette, S., Peerboom, C., Denis, L., Benali, S., Raquez, J. M., ... & Goole, J. (2019). Investigation of the parameters used in fused deposition modeling of poly (lactic acid) to optimize 3D printing sessions. International Journal of Pharmaceutics, 565, 367-377. https://doi.org/10.1016/j.ijpharm.2019.05.008

[4]. Chohan, J. S., Singh, R., & Boparai, K. S. (2016). Parametric optimization of fused deposition modeling and vapour smoothing processes for surface finishing of biomedical implant replicas. Measurement, 94, 602-613. https://doi.org/10.1016/j.measurement.2016.09.001

[5]. Deswal, S., Narang, R., & Chhabra, D. (2019). Modeling and parametric optimization of FDM 3D printing process using hybrid techniques for enhancing dimensional preciseness. International Journal on Interactive Design and Manufacturing (IJIDeM), 13(3), 1197-1214. https://doi.org/10.1007/s12008-019-00536-z

[6]. Ding, S., Zou, B., Wang, P., & Ding, H. (2019). Effects of nozzle temperature and building orientation on mechanical properties and microstructure of PEEK and PEI printed by 3D-FDM. Polymer Testing, 78, Article 105948. https://doi.org/10.1016/j.polymertesting.2019.105948

[7]. Gurrala, P. K., & Regalla, S. P. (2014). Multi-objective optimisation of strength and volumetric shrinkage of FDM parts: A multi-objective optimization scheme is used to optimize the strength and volumetric shrinkage of FDM parts considering different process parameters. Virtual and Physical Prototyping, 9(2), 127-138. https://doi.org/10.1080/17452759.2014.898851

[8]. Heidari-Rarani, M., Rafiee-Afarani, M., & Zahedi, A. M. (2019). Mechanical characterization of FDM 3D printing of continuous carbon fiber reinforced PLA composites. Composites Part B: Engineering, 175, Article 107147. https://doi.org/10.1016/j.compositesb.2019.107147

[9]. Hwang, C. L., & Yoon, K. (1981). Multiple Attribute Decision Making Methods and Applications, NY: Springer.

[10]. Ju-Long, D. (1982). Control problems of grey systems. Systems & Control Letters, 1(5), 288-294. https://doi.org/10.1016/S0167-6911(82)80025-X

[11]. Kaveh, M., Badrossamay, M., Foroozmehr, E., & Etefagh, A. H. (2015). Optimization of the printing parameters affecting dimensional accuracy and internal cavity for HIPS material used in fused deposition modeling processes. Journal of Materials Processing Technology, 226, 280-286. https://doi.org/10.1016/j.jmatprotec.2015.07.012

[12]. Kim, N. P., Cho, D., & Zielewski, M. (2019). Optimization of 3D printing parameters of Screw Type Extrusion (STE) for ceramics using the Taguchi method. Ceramics International, 45(2), 2351-2360. https://doi.org/10.1016/j.ceramint.2018.10.152

[13]. Liu, Z., Lei, Q., & Xing, S. (2019). Mechanical characteristics of wood, ceramic, metal and carbon fiber-based PLA composites fabricated by FDM. Journal of Materials Research and Technology, 8(5), 3741-3751. https://doi.org/10.1016/j.jmrt.2019.06.034

[14]. Mahmood, S., Qureshi, A. J., & 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

[15]. Malik, A., & Manna, A. (2018). Multi-response optimization of laser-assisted jet electrochemical machining parameters based on gray relational analysis. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 40(3), 1-21. https://doi.org/10.1007/s40430-018-1069-9

[16]. Manivannan, R., & Kumar, M. P. (2017). Multiattribute decision-making of cryogenically cooled micro- EDM drilling process parameters using TOPSIS method. Materials and Manufacturing Processes, 32(2), 209-215. https://doi.org/10.1080/10426914.2016.1176182

[17]. Mansour, M., Tsongas, K., & Tzetzis, D. (2019). Measurement of the mechanical and dynamic properties of 3D printed polylactic acid reinforced with graphene. Polymer-Plastics Technology and Materials, 58(11), 1234-1244. https://doi.org/10.1080/03602559.2018.1542730

[18]. Mausam, K., Sharma, K., Bharadwaj, G., & Singh, R. P. (2019). Multi-objective optimization design of diesinking electric discharge machine (EDM) machining parameter for CNT-reinforced carbon fibre nanocomposite using grey relational analysis. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 41(8), 1-8. https://doi.org/10.1007/s40430-019-1850-4

[19]. Mohamed, O. A., Masood, S. H., & Bhowmik, J. L. (2016a). Experimental investigations of process parameters influence on rheological behavior and dynamic mechanical properties of FDM manufactured parts. Materials and Manufacturing Processes, 31(15), 1983-1994. https://doi.org/10.1080/10426914.2015.1127955

[20]. Mohamed, O. A., Masood, S. H., & Bhowmik, J. L. (2016b). Mathematical modeling and FDM process parameters optimization using response surface methodology based on Q-optimal design. Applied Mathematical Modelling, 40(23-24), 10052-10073. https://doi.org/10.1016/j.apm.2016.06.055

[21]. Mohamed, O. A., Masood, S. H., & Bhowmik, J. L. (2016c). Optimization of fused deposition modeling process parameters for dimensional accuracy using Ioptimality criterion. Measurement, 81, 174-196. https://doi.org/10.1016/j.measurement.2015.12.011

[22]. Nagendra, J., & Prasad, M.S.G. (2020). FDM process parameter optimization by Taguchi technique for augmenting the mechanical properties of nylon–aramid composite used as filament material. Journal of the Institution of Engineers (India): Series C, 101, 313–322. https://doi.org/10.1007/s40032-019-00538-6

[23]. Parthiban, K., Duraiselvam, M., & Manivannan, R. (2018). TOPSIS based parametric optimization of laser micro-drilling of TBC coated nickel based superalloy. Optics & Laser Technology, 102, 32-39. https://doi.org/10.1016/j.optlastec.2017.12.012

[24]. Ransikarbum, K., Ha, S., Ma, J., & Kim, N. (2017). Multi-objective optimization analysis for part-to-Printer assignment in a network of 3D fused deposition modeling. Journal of Manufacturing Systems, 43, 35-46. https://doi.org/10.1016/j.jmsy.2017.02.012

[25]. Rao, R. V., & Rai, D. P. (2016). Optimization of fused deposition modeling process using teaching-learningbased optimization algorithm. Engineering Science and Technology, an International Journal, 19(1), 587-603. https://doi.org/10.1016/j.jestch.2015.09.008

[26]. Samykano, M., Selvamani, S. K., Kadirgama, K., Ngui, W. K., Kanagaraj, G., & Sudhakar, K. (2019). Mechanical property of FDM printed ABS: influence of printing parameters. The International Journal of Advanced Manufacturing Technology, 102(9), 2779-2796. https://doi.org/10.1007/s00170-019-03313-0

[27]. Sindhu, D., Thakur, L., & Chandna, P. (2019). Multiobjective optimization of rotary ultrasonic machining parameters for quartz glass using Taguchi-Grey relational analysis (GRA). Silicon, 11(4), 2033-2044 .https://doi.org/10.1007/s12633-018-0019-6

[28]. Sivaiah, P., & Chakradhar, D. (2017). Multi-objective optimisation of cryogenic turning process using Taguchibased grey relational analysis. International Journal of Machining and Machinability of Materials, 19(4), 297-312.

[29]. Sivaiah, P., & Chakradhar, D. (2018). Multi performance characteristics optimization in cryogenic turning of 17-4 PH stainless steel using Taguchi coupled grey relational analysis. Advances in Materials and Processing Technologies, 4(3), 431-447. https://doi.org/10.1080/2374068X.2018.1452132

[30]. Sivaiah, P., & Chakradhar, D. (2019). Performance improvement of cryogenic turning process during machining of 17-4 PH stainless steel using multi objective optimization techniques. Measurement, 136, 326-336. https://doi.org/10.1016/j.measurement.2018.12.094

[31]. Thakur, A., Manna, A., & Samir, S. (2020). Multiresponse optimization of turning parameters during machining of EN-24 steel with SiC nanofluids based minimum quantity lubrication. Silicon, 12(1), 71-85. https://doi.org/10.1007/s12633-019-00102-y

[32]. Uzun, G. (2019). Analysis of grey relational method of the effects on machinability per formance on austempered vermicular graphite cast irons. Measurement, 142, 122-130. https://doi.org/10.1016/j.measurement.2019.04.059

[33]. Wang, Z., Zhang, T., Yu, T., & Zhao, J. (2020). Assessment and optimization of grinding process on AISI 1045 steel in terms of green manufacturing using orthogonal experimental design and grey relational analysis. Journal of Cleaner Production, 253, Article 119896. https://doi.org/10.1016/j.jclepro.2019.119896

[34]. Yuvaraj, N., & Kumar, M. P. (2015). Multiresponse optimization of abrasive water jet cutting process parameters using TOPSIS approach. Materials and Manufacturing Processes, 30(7), 882-889. https://doi.org/10.1080/10426914.2014.994763

If you have access to this article please login to view the article or kindly login to purchase the article

Single Article

	North Americas,UK, Middle East,Europe		India	Rest of world
	USD	EUR	INR	USD-ROW
Online	15	15

ADD TO CART

Options for accessing this content:

If you would like institutional access to this content, please recommend the title to your librarian.
If you already have i-manager's user account: Login above and proceed to purchase the article.
New Users: Please register, then proceed to purchase the article.