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Purpose: The purpose of the research was to establish the possibilities and limitations of using 3D printing technology to fabricate glass domes, specifically for creating complex glass architectural forms and structures both on Earth and in extreme conditions, such as the surface of Mars. Design/methodology/approach: The study examines various 3D printing methods for producing glass domes, focusing on their performance in extreme environments. It highlights the capabilities and constraints of additive manufacturing technologies for building intricate glass structures, particularly on Martian applications. Findings: The study found that several 3D printing technologies show potential for fabricating glass structures in extreme environments. Among them, SLA and DLP methods offer the highest transparency and structural integrity, achieving up to 90% transparency after sintering, making them viable for use on Mars. The main conclusion is that although 3D printing glass domes on Mars is technologically feasible, significant challenges, such as minimising defects and optimising material properties for Martian conditions, must be overcome. Research limitations/implications: The study is constrained by current 3D printing technology and the absence of real-world tests in Martian conditions. Future research should aim to enhance the mechanical properties of printed glass, refine the printing processes, and conduct experiments in environments that simulate Martian conditions. Practical implications: The findings suggest that 3D printing could transform space construction by enabling the autonomous fabrication of glass domes using Martian resources. This technology could be crucial in building sustainable habitats for future space missions. Originality/value: The article explores the use of 3D printing for constructing glass domes on Mars, highlighting the potential of Martian resources for future habitats.
Wydawca
Rocznik
Tom
Strony
31--41
Opis fizyczny
Bibliogr. 48 poz.
Twórcy
autor
- Department of Building Structures and Bridges, Institute of Building and Environmental Engineering, Lviv Polytechnic National University,12 Bandera Street, Lviv, 79013, Ukraine
autor
- Department of Strength of Materials and Structural Mechanics, Institute of Building and Environmental Engineering, Lviv Polytechnic National University, 12 Bandera Street, Lviv, 79013, Ukraine
autor
- Department of Building Structures and Bridges, Institute of Building and Environmental Engineering, Lviv Polytechnic National University,12 Bandera Street, Lviv, 79013, Ukraine
Bibliografia
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- [32] G. Sodeifian, S. Ghaseminejad, A.A. Yousefi, Preparation of polypropylene/short glass fiber composite as Fused Deposition Modeling (FDM) filament, Results in Physics 12 (2019) 205-222. DOI: https://doi.org/10.1016/j.rinp.2018.11.065
- [33] J. Klein, M. Stern, G. Franchin, M. Kayser, C. Inamura, S. Dave, J.C. Weaver, P. Houk, P. Colombo, M. Yang, N. Oxman, Additive Manufacturing of Optically Transparent Glass, 3D Printing and Additive Manufacturing 2/3 (2015) 92-105. DOI: https://doi.org/10.1089/3dp.2015.0021
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- [40] X. Wen, B. Zhang, W. Wang, F. Ye, S. Yue, H. Guo, G. Gao, Y. Zhao, Q. Fang, C. Nguyen, X. Zhang, J. Bao, J.T. Robinson, P.M. Ajayan, J. Lou, 3D-printed silica with nanoscale resolution, Nature Materials 20 (2021) 1506-1511. DOI: https://doi.org/10.1038/s41563-021-01111-2
- [41] I. Priyadarshini, V. Puri, Mars weather data analysis using machine learning techniques, Earth Science Informatics 14 (2021) 1885-1898. DOI: https://doi.org/10.1007/s12145-021-00643-0
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- [45] A.A. Mir, J. Mushtaq, A. Dar, M. Patel, A quantitative investigation of methane gas and solid waste management in mountainous Srinagar city-A case study, Journal of Material Cycles and Waste Management 25/1 (2023) 535-549. DOI: https://doi.org/10.1007/s10163-022-01516-4
- [46] A.A. Mir, M. Patel, Machine learning approaches for adequate prediction of flow resistance in alluvial channels with bedforms, Water Science and Technology 89/2 (2024) 290-318. DOI: https://doi.org/10.2166/wst.2023.396
- [47] R. Kumar, A. Rathore, R. Singh, A.A. Mir, R. Tipu, M. Patel, Prognosis of flow of fly ash and blast furnace slag-based concrete: leveraging advanced machine learning algorithms, Asian Journal of Civil Engineering 25/3 (2024) 2483-2497. DOI: https://doi.org/10.1007/s42107-023-00922-9
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Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-54462fbe-a413-41d1-9e8b-48b9343d45dd
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