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Experimental investigation and statistical modelling for assessing the tensile properties of FDM fabricated parts

Nikolaos A. Fountas, P.K. Kostazos, H. Pavlidis, V. Antoniou, D.E. Manolakos, Nikolaos M. Vaxevanidis

2020Procedia Structural Integrity31 citationsDOIOpen Access PDF

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.

Topics & Concepts

Fused deposition modelingUltimate tensile strengthFractional factorial designDesign of experimentsBrittlenessFactorial experimentMaterials scienceResponse surface methodologyCurvatureArtificial neural networkComposite materialStructural engineering3D printingMechanical engineeringComputer scienceEngineeringMathematicsArtificial intelligenceMachine learningStatisticsGeometryAdditive Manufacturing and 3D Printing TechnologiesManufacturing Process and OptimizationAdditive Manufacturing Materials and Processes
Experimental investigation and statistical modelling for assessing the tensile properties of FDM fabricated parts | Litcius