PSI - Issue 26

Available online at www.sciencedirect.com Structural Int grity Procedia 00 (2019) 000–000 Available online at www.sciencedirect.com ScienceDirect Structural Integrity Procedia 00 (2019) 000–000 Available online at www.sciencedirect.com ScienceDirect

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Procedia Structural Integrity 26 (2020) 139–146

© 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 © 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 Keywords: Fused deposition modeling-FDM; 3D printing; tensile strength; regression modelling; intelligent algorithms; neural network Abstract Owing to its ability to manufacture omplex parts without expensive tooling requ rement or human intervention, fused depo ition m d lling (FDM) is gaining di tinct advantag i manufacturing industry. A it o curs to any other engineering proc ss, the pro erties of FDM-built pr ducts exhibit high dependence on process parameters which may be improved by setting suitable lev l for parameters associated to FDM. Anisotropic and brittle natu of build part m kes it essential to examine the effect of process parameters to the resistanc of tensil loading for improving strength of functional parts. This paper focuses o the experimen al study of ex mining the effect of five fus d depositio modeling p ameters such as lay r height, shell thickness, infill densi y, orientation angle and printing speed on the tensile strength of standard ASTM 638-10 type 1 tensile pecimens. The experim ntal study involved a fractional factorial design involving 16 runs. This desi n was then converted to a ustom re p nse surface esign to examine the non-linearity presented by the curvature when examining independent variables in ntinu us form. The study not only gives an insight concerning the complex dependency of ten ile load by the process parameters corresponding to FDM but also generates a statistic lly validated regr ssio model. The regression model adequately explains the variation and the non-linear i fluence of FDM parameters on tensil stre g h and thus, it can be implemented to find optimal parameter settings with the use of any artificial intelligent algorithm or neural network. © 2020 The Authors. Published by Elsevier B.V. This is an ope access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) P er-review under resp sibility of MedFract1 organizers Keywords: Fused deposition modeling-FDM; 3D printing; tensile strength; regression modelling; intelligent algorithms; neural network The 1 st Mediterranean Conference on Fracture and Structural Integrity, MedFract1 Experimental investigation and statistical modelling for assessing the tensile properties of FDM fabricated parts N.A. Fountas a , P. Kostazos b , H. Pavlidis a , V. Antoniou a , D.E. Manolakos b , N.M.Vaxevanidis a * a Laboratory of Manufacturing Processes & Machine Tools (LMProMaT), Department of Mechanical Engineering Educators, School of Pedagogical and Technological Education (ASPETE), Amarousion GR 151 22, Greece b School of Mechanical Engineering, National Technical University of Athens (NTUA), Athens, Greece Abstract Owing to its ability to manufacture complex parts without expensive tooling requirement or human intervention, fused deposition 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. The 1 st Mediterranean Conference on Fracture and Structural Integrity, MedFract1 Experimental investigation and statistical modelling for assessing the tensile properties of FDM fabricated parts N.A. Fountas a , P. Kostazos b , H. Pavlidis a , V. Antoniou a , D.E. anolakos b , N.M.Vaxevanidis a * a Laboratory of Manufacturin Processes & Machine Tools (LMProMaT), Depart ent of Mechanical Engin ering Educators, School of Pedagogical nd Technolo ical Education (ASPETE), Amar usion GR 151 22, Gr ece b School of Mechanical Engineering, National Technical University of Athens (NTUA), Athens, Greece

* Corresponding author. Tel.: +30 210 2896841. E-mail address: vaxev@aspete.gr * Correspon ing author. Tel.: +30 210 2896841. E-mail address: vaxev@aspete.gr

2452-3216 © 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 2452-3216 © 2020 The Authors. Published by Elsevier B.V. This is an ope acces article under th CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of MedFract1 organizers

2452-3216 © 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 10.1016/j.prostr.2020.06.017