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Abstrakty
The production of functional parts, including those employed by the biomedical industry has been achieved a promising candidate in Fused Deposition Modelling (FDM). The essential properties of these biomedical parts which manufactured by additive manufacturing as compared to some other conventional manufacturing processes depend on structural and process parameters rather than material properties alone. Regarding to the evaluation the flexural strength of medical-grade, Polymethylmethacrylate PMMA has been received relatively very little investigation to date. PMMA is a biocompatible filament that be used in manufacturing of patient-specific implants such as dental prosthesis and orthopaedic implants. The proposed work explores the effect of three process parameters that vary with respect of three levels on the flexural strength. These levels can be specified by layer height (120, 200, 280 µm), infill density (40, 65, 90 %) and skewing angle (0º, 45º, 90º) on the flexural strength of medical-grade PMMA. Maximum and minimum flexural strength that be obtained in this work about (93 and 57 MPa) respectively. The analysis of variance (ANOVA) results shows that the most effective factor is the layer height followed by infill density. The flexural strength rises significantly with decreases layer height and the skewing angle is in zero direction. The process parameters have been optimized through utilizing of genetic algorithms. The optimal results that emerged based on genetic algorithm technique are approximately (276 μm) as layer height, (46 %) infill density and skewing angle (89 º) which maximize the flexural strength to (97 MPa) at crossover for ten generation.
Wydawca
Rocznik
Tom
Strony
349--359
Opis fizyczny
Bibliogr. 21 poz., fig., tab.
Twórcy
autor
- Production Engineering and Metallurgy Department, University of Technology, Baghdad, Iraq
autor
- Biomedical Engineering Department, University of Technology, Baghdad, Iraq
Bibliografia
- 1. Zadpoor AA. Design for additive bio-manufacturing: from patient-specific medical devices to rationally designed meta-biomaterials. International Journal of Molecular Sciences. 2017 Jul 25;18(8):1607.https://doi: 10.3390/ijms18081607.
- 2. Rajan K, Samykano M, Kadirgama K, Harun WS, Rahman MM. Fused deposition modeling: process, materials, parameters, properties, and applications. The International Journal of Advanced Manufacturing Technology. 2022 May;120(3-4):1531-70. https//doi: 10.1007/s00170-022-08860-7.
- 3. Uthayakumar M, Raj SA, Ko TJ, Kumaran ST, Davim JP. Handbook of research on green engineering techniques for modern manufacturing. 2019. https//10.4018/978-1-5225-5445-5.
- 4. Karupaiah V, Narayanan V. Quasi-static and dynamic mechanical analysis of 3D printed ABS and carbon fiber reinforced ABS composites. Materiale Plastice. 2022 Sep 1;59(3):152-79. https//doi: 10.37358/MP.22.3.5613.
- 5. Lu B, Lamnawar K, Maazouz A, Zhang H. Revealing the dynamic heterogeneity of PMMA/PVDF blends: from microscopic dynamics to macroscopic properties. Soft Matter. 2016;12(13):3252-64. https//doi: 10.1039/C5SM02659H.
- 6. Yousfi M, Belhadj A, Lamnawar K, Maazouz A. 3D printing of PLA and PMMA multilayered model polymers: An innovative approach for a better-controlled pellet multi-extrusion process. 2021. https//doi: 10.25518/esaform21.1024.
- 7. Sukindar NA, Samsudin NM, Shaharuddin SI, Kamaruddin S. The effects of FDM printing parameters on the compression properties of polymethylmethacrylate (PMMA) using finite element analysis. International Journal of Integrated Engineering. 2022 Jun 12;14(2):86-92. https// doi: 10.30880/ijie.2022.14.02.013.
- 8. Linh NT, Huy KD, Dung NT, Luong NX, Hoang T, Tham DQ. Fabrication and characterization of PMMA/ZrO2 nanocomposite 3D printing filaments. Vietnam Journal of Chemistry. 2023 Aug;61(4):461-9. https// doi: 10.1002/vjch.202200185.
- 9. Archana R, Baldia M, Jeeva JB, Joseph M. Strength analysis of cranioplasty PMMA flap material. Materials Today: Proceedings. 2019 Jan 1;15:167-72. https// doi: 10.1016/j.matpr.2019.04.188.
- 10. Deshwal S, Kumar A, Chhabra D. Exercising hybrid statistical tools GA-RSM, GA-ANN and GA-AN-FIS to optimize FDM process parameters for tensile strength improvement. CIRP Journal of Manufacturing Science and Technology. 2020 Nov 1;31:189-99. https// doi: 10.1016/j.cirpj.2020.05.009.
- 11. Kumar V, Kumar A, Chhabra D, Shukla P. Improved biobleaching of mixed hardwood pulp and process optimization using novel GA-ANN and GA-AN-FIS hybrid statistical tools. Bioresource technology. 2019 Jan 1;271:274-82. https// doi: 10.1016/j. biortech.2018.09.115.
- 12. Hamed MA, Abbas TF. The impact of FDM proces parameters on the compression strength of 3D printed PLA filaments for dental applications. Advances in Science and Technology. Research Journal. 2023;17(4). https// doi: 10.12913/22998624/169468.
- 13. Abdullah Z, Ting HY, Ali MA, Fauadi MH, Kasim MS, Hambali A, Ghazaly MM, Handoko F. The effect of layer thickness and raster angles on tensile strength and flexural strength for fused deposition modeling (FDM) parts. Journal of Advanced Manufacturing Technology (JAMT). 2018;12(1 (4)):147-58.
- 14. Nugroho A, Ardiansyah R, Rusita LA, Larasati IL. Effect of layer thickness on flexural properties of PLA (PolyLactid Acid) by 3D printing. InJournal of Physics: Conference Series 2018 Nov 1 (Vol.1130, p. 012017). IOP Publishing. https// doi: 10.1088/1742-6596/1130/1/012017.
- 15. Dev S, Srivastava R. Optimization of fused deposition modeling (FDM) process parameters for flexural strength. Materials Today: Proceedings. 2021 Jan 1;44:3012-6. https// doi: 10.1016/j.matpr.2021.02.436.
- 16. Mohankumar HR, Benal MGM, Pradeepkumar GS, Tambrallimath V, Ramaiah K, Yunus Khan TM, Bhutto JK, Ali MA. Effect of short glass fiber addition on flexural and impact behavior of 3D printed polymer composites. ACS omega. 2023 Mar 1;8(10):9212-20. https//doi: 10.1021/acsomega.2c07227.
- 17. Sofia J, Kumar KK, Ethiraj N, Nikolova MP. Characterisation of PLA-PMMA laminate without resin fabricated by FDM. Academic Journal of Manufacturing Engineering. 2023;21(3).
- 18. Abbas AS, Mahdi BS, Abbas HH, Sayyid FF, Mustafa AM, Annon IA, Abdulsahib YM, Resen AM, Hanoon MM, Obaeed NH. Corrosion behavior optimization by nanocoating layer for low carbon steel in acid and salt media. Corrosion Science and Technology. 2023; 22(1): 1-9. https://doi.org/10.14773/cst.2023.22.1.1.
- 19. Obaeed NH, Abdullah MA, Muath M, Adnan M, Amir H. Study The effect of process parameters of CNC milling surface generation using Al-alloy 7024. Diyala Journal of Engineering Sciences. 2019 Sep 1:103-12. https// doi: 10.24237/djes.2019.12312.
- 20. Alduroobi AA, Ibraheem MQ, Obaeed NH. Predict the best variants of cutting in turning process using genetic algorithm technique. In: 2nd International Conference for Engineering, Technology and Sciences of Al-Kitab (ICETS), 2018, Dec 4, pp. 33-38. IEEE. https// doi: 10.1109/ICETS.2018.8724617.
- 21. Haldurai L, Madhubala T, Rajalakshmi R. A study on genetic algorithm and its applications. Int. J. Comput. Sci. Eng. 2016 Oct;4(10):139-43. https//doi: 10.56726/irjmets32980.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-723732f6-d447-44de-aa63-6284bd7922db