OPTIMIZATION OF PROCESS PARAMETERS OF 3D PRINTED POLYLACTIC ACID PARTS USING GENETIC ALGORITHM
DOI:
https://doi.org/10.11113/aej.v15.22422Keywords:
3D Printed Parts, Optimization, Genetic Algorithm, Single Objective function, Multi Objective functionAbstract
In this paper, the optimization of input process parameters of 3D printed parts of Polylactic Acid in Fused Deposition Modeling is studied using a genetic algorithm. The work emphasizes on influence of input process parameters on Young’s modulus, breakage tension, and breakage deformation. Optimization of process parameters is studied for single objectives such as Young's modulus, break in tension, and breakage deformation and also optimizing combined three objective functions simultaneously. The regression model is used as a fitness function to optimize the objectives. Built-in functions in MATLAB ‘ga’ and ‘gamultiobj’ are applied for optimization of single and multi-objectives respectively. The study reveals that simultaneous optimization of three objectives reduces a 27.27% breakage deformation, 13.7% Young’s modulus, and 55.5% break-in tension as compared to optimization of individual objectives.
References
Drumright, R. E., Gruber, P. R., & Henton, D. E. 2000. Polylactic acid technology. Advanced materials. 12(23): 1841-1846. DOI: https://doi.org/10.1002/1521 4095(200012)12:23<1841::AID ADMA1841>3.0.CO;2-E
Averett, R. D., Realff, M. L., Jacob, K., Cakmak, M., & Yalcin, B. 2011. The mechanical behavior of poly (lactic acid) unreinforced and nanocomposite films subjected to monotonic and fatigue loading conditions. Journal of composite materials, 45(26): 2717-2726. DOI: https://doi.org/10.1177/0021998311410464
Farah, S., Anderson, D. G., & Langer, R. 2016. Physical and mechanical properties of PLA, and their functions in widespread applications—A comprehensive review. Advanced drug delivery reviews, 107: 367-392. DOI: http://dx.doi.org/10.1016/j.addr.2016.06.012
Mirkhalaf, S. M., & Fagerström, M. 2021. The mechanical behavior of polylactic acid (PLA) films: fabrication, experiments and modelling. Mechanics of Time-Dependent Materials, 25(2): 119-131. DOI: https://doi.org/10.1007/s11043-019-09429-w
Gibson, I., Rosen, D. W., & Stucker, B. 2010. Additive manufacturing technologies: 3D printing, rapid prototyping, and direct digital manufacturing. Springer Science & Business Media. (ISBN:1493921126)
Hopkinson, N., Hague, R., & Dickens, P. 2006. Rapid manufacturing: An industrial revolution for the digital age. John Wiley & Sons.
Saquib Rouf, Ankush Raina, Mir Irfan Ul Haq, Nida Naveed, Sudhanraj Jeganmohan, Aysha Farzana Kichloo, 2022. 3D printed parts and mechanical properties: Influencing parameters, sustainability aspects, global market scenario, challenges and applications, Advanced Industrial and Engineering Polymer Research. 5(3): 143-158. DOI: https://doi.org/10.1016/j.aiepr.2022.02.001
Elisabetta Monaldo, Maurizio Ricci, Sonia Marfia. 2023. Mechanical properties of 3D printed polylactic acid elements: Experimental and numerical insights. Mechanics of Materials. 177: 104551. DOI: https://doi.org/10.1016/j.mechmat.2022.104551
Travieso-Rodriguez, J.A., Jerez-Mesa Ramon, Jordi Llumà, Oriol Traver Ramos, Gómez-Gras, Giovanni, Joan Josep Roa Rovira. 2019. Mechanical Properties of 3D-Printing Polylactic Acid Parts subjected to Bending Stress and Fatigue Testing. Materials. 12(23): 3859. DOI: https://doi.org/10.3390/ma12233859
Sandanamsamy L., Mogan J., Rajan K., Harun W. S. W., Ishak I., Romlay F. R., M. Samykano, Kadirgama K. 2023. Effect of process parameter on tensile properties of FDM printed PLA. Materials Today: Proceedings. DOI: https://doi.org/10.1016/j.matpr.2023.03.217
Luca Fontana, Paolo Minetola, Luca Iuliano, Serena Rifuggiato, Mankirat Singh Khandpur, Vito Stiuso. 2022. An investigation of the influence of 3D printing parameters on the tensile strength of PLA material, Materials Today: Proceedings. 57(2): 657-663, DOI: https://doi.org/10.1016/j.matpr.2022.02.078
Tofail S. A. M., Koumoulos E. P., Bandyopadhyay A., Bose S., O'Donoghue L., Charitidis C. 2018. Additive manufacturing: scientific and technological challenges, market uptake and opportunities. Materials Today. 21(1): 22-37. DOI: https://doi.org/10.1016/j.mattod.2017.07.001
Kumar K. R., Mohanavel V., Kiran K. 2022. Mechanical properties and characterization of polylactic acid/carbon fiber composite fabricated by fused deposition modeling. Journal of Materials Engineering and Performance, 31(6): 4877-4886.
Marșavina L., Vălean C., Mărghitaș M., Linul E., Razavi N., Berto F., Brighenti R. 2022. Effect of the manufacturing parameters on the tensile and fracture properties of FDM 3D-printed PLA specimens. Engineering Fracture Mechanics. 274: 108766.
Tünçay M.M. 2024. An Investigation of 3D Printing Parameters on Tensile Strength of PLA Using Response Surface Method. Journal of Materials Engineering and Performance.33: 6249–6258. DOI: https://doi.org/10.1007/s11665-023-08395-2
Ambade V., Rajurkar S., Awari G., B. Yelamasetti, S. Shelare. 2023. Influence of FDM process parameters on tensile strength of parts printed by PLA material. International Journal on Interactive Design and Manufacturing. DOI: https://doi.org/10.1007/s12008-023-01490-7
Saifuddin Khan, Ketan Joshi, Samadhan Deshmukh. 2022. A comprehensive review on effect of printing parameters on mechanical properties of FDM printed parts. Materials Today: Proceedings. 50(5): 2119-2127, DOI: https://doi.org/10.1016/j.matpr.2021.09.433.
Kumaresan R., Samykano M., Kadirgama K., Pandey A. K., Rahman M. M. 2023. Effects of printing parameters on the mechanical characteristics and mathematical modeling of FDM-printed PETG. The International Journal of Advanced Manufacturing Technology, 128(7-8): 3471-3489. DOI: https://doi.org/10.1007/s00170-023-12155-w
Manav Doshi, Ameya Mahale, Suraj Kumar Singh, Samadhan Deshmukh. 2022. Printing parameters and materials affecting mechanical properties of FDM-3D printed Parts: Perspective and prospects. Materials Today: Proceedings. 50(5): 2269-2275. DOI: https://doi.org/10.1016/j.matpr.2021.10.003.
Kristiawan R. B., Imaduddin F., Ariawan D., Ubaidillah Arifin Z. 2021. A review on the fused deposition modeling (FDM) 3D printing: Filament processing, materials, and printing parameters. Open Engineering, 11(1): 639-649. DOI: https://doi.org/10.1515/eng-2021-0063
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(3): 64. DOI: https://doi.org/10.3390/jmmp3030064
Camposeco-Negrete C. 2020. Optimization of printing parameters in fused deposition modeling for improving part quality and process sustainability. The International Journal of Advanced Manufacturing Technology. 108(7): 2131-2147. DOI: https://doi.org/10.1007/s00170-020-05555-9
Kafshgar A. R., Rostami S., Aliha M. R. M., Berto, F. 2021. Optimization of properties for 3D printed PLA material using taguchi, anova and multi-objective methodologies. Procedia Structural Integrity. 34: 71-77. DOI: https://doi.org/10.1016/j.prostr.2021.12.011
Yodo N., & Dey A. 2021. Multi-objective optimization for FDM process parameters with evolutionary algorithms. Fused Deposition Modeling Based 3D Printing. 419-444. DOI: https://doi.org/10.1007/978-3-030-68024-4_22
Raghavendra B. 2020. Hybrid genetic algorithm for bi-criteria objectives in scheduling process. Management and Production Engineering Review, 11. DOI: 10.24425/mper.2020.133733
Raghavendra B.V. 2019. Effect of Crossover Probability on Performance of Genetic Algorithm in Scheduling of Parallel Machines for BI- Criteria Objectives. International Journal of Engineering and Advanced Technology. 9(1): 2827–2831. DOI: 10.35940/ijeat.A9801.109119
Puttaswamy S., & Raghavendra V. 2021. Effect of Machining Parameters On Surface Roughness, Power Consumption, and Material Removal Rate of Aluminium 6065-Si-Mwcnt Metal Matrix Composite In Turning Operations. IIUM Engineering Journal. 22(2): 283–293. DOI: https://doi.org/10.31436/iiumej.v22i2.1640
Eguren J.A., Esnaola A., & Unzueta G. 2020. Modelling of an additive 3D-printing process based on design of experiments methodology. Quality Innovation Prosperity. 24(1): 128-151. DOI: https://doi.org/10.12776/qip.v24i1.1435
