期刊
1ST MEDITERRANEAN CONFERENCE ON FRACTURE AND STRUCTURAL INTEGRITY (MEDFRACT1)
卷 26, 期 -, 页码 139-146出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.prostr.2020.06.017
关键词
Fused deposition modeling-FDM; 3D printing; tensile strength; regression modelling; intelligent algorithms; neural network
modelling (FDM) is gaining distinct advantage in manufacturing industry. As it occurs to any other engineering process, the properties of FDM-built products exhibit high dependence on process parameters which may be improved by setting suitable levels for parameters associated to FDM. Anisotropic and brittle nature of build part makes it essential to examine the effect of process parameters to the resistance of tensile loading for improving strength of functional parts. This paper focuses on the experimental study of examining the effect of five fused deposition modeling parameters such as layer height, shell thickness, infill density, orientation angle and printing speed on the tensile strength of standard ASTM 638-10 type 1 tensile specimens. The experimental study involved a fractional factorial design involving 16 runs. This design was then converted to a custom response surface design to examine the non-linearity presented by the curvature when examining independent variables in continuous form. The study not only gives an insight concerning the complex dependency of tensile load by the process parameters corresponding to FDM but also generates a statistically validated regression model. The regression model adequately explains the variation and the non-linear influence of FDM parameters on tensile strength and thus, it can be implemented to find optimal parameter settings with the use of any artificial intelligent algorithm or neural network. (C) 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of MedFract1 organizers
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