Conductive Additive Manufactured Acrylonitrile Butadiene Styrene Filaments: Statistical Approach to Mechanical and Electrical Behaviors.
| Autor: | Ulkir O; Department of Electric and Energy, Technical Sciences Vocational School, Mus Alparslan University, Mus, Turkey. |
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| Jazyk: | angličtina |
| Zdroj: | 3D printing and additive manufacturing [3D Print Addit Manuf] 2023 Dec 01; Vol. 10 (6), pp. 1423-1438. Date of Electronic Publication: 2023 Dec 11. |
| DOI: | 10.1089/3dp.2022.0287 |
| Abstrakt: | Additive manufacturing is a process in which digital three-dimensional (3D) design data are used to build a component in layers by accumulating materials. There are many materials used in additive manufacturing technology. The most basic features that distinguish these materials are their strength and electrical behavior. They can be strong or flexible, resistant to abrasion, depending on the application used. Recently, 3D printing filament and polymeric composite materials combined with carbon nanostructures with electrical conductivity have been used. In this study, acrylonitrile butadiene styrene (ABS), a carbon black-filled conductive material with high strength and hardness, was preferred. The aim in this study is to focus on the mechanical and electrical behavior of the material processed in filament form. Fabrication of samples was done using a fused deposition modeling-based printer that controls filament orientation. Different experimental studies were conducted: (1) mechanical tests to determine the maximum tensile strength values of the samples; and (2) electrical tests to analyze the electrical resistances of the samples. In the design of the first experiment, infill volume, layer height, infill type, and printing direction were determined as factors affecting strength. In the design of the second experiment, the length, nozzle temperature, and measurement temperature were determined as the factors affecting the electrical resistance. Statistical analysis of the measured data was performed to evaluate the overall result of the experiments. Finally, a prediction model of real-time tensile strength and resistance values was created using machine learning algorithms. These algorithms are Gaussian Process Regression and Support Vector Machine. The results confirmed the known linear dependence of electrical resistance on the length of the 3D-printed conductive ABS samples and showed how changing the fabrication settings affected the strength values. Competing Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. (Copyright 2023, Mary Ann Liebert, Inc., publishers.) |
| Databáze: | MEDLINE |
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