Advances in Production Engineering & Management
Volume 11 | Number 3 | September 2016 | pp 227–238
http://dx.doi.org/10.14743/apem2016.3.223
Investigation of dynamic elastic deformation of parts processed by fused deposition modeling additive manufacturing
Mohamed, Omar A.; Masood, Syed H.; Bhowmik, Jahar L.
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A B S T R A C T
Fused deposition modeling (FDM) has been recognized as an effective technology to manufacture 3D dimensional parts directly from a digital computer aided design (CAD) model in a layer-by-layer style. Although it has become a significantly important manufacturing process, but it is still not well accepted additive manufacturing technology for load-carrying parts under dynamic and cyclic conditions due to many processing parameters affecting the part properties. The purpose of this study is to characterize the FDM manufactured parts by detecting how the individual and interactive FDM process parameters will influence the performance of manufactured products under dynamic and cyclic conditions. Experiments were conducted through fractional factorial design and artificial neural network (ANN). Effect of each parameter on the dynamic modulus of elasticity was investigated using analysis of variance (ANOVA) technique. Furthermore, optimal processing parameters were determined and validated by conducting verification experiment. The results showed that both ANN and fractional factorial models provided good quality predictions, yet the ANN showed the superiority of a properly trained ANN in capturing the nonlinear relationship of the system over fractional factorial for both data fitting and estimation capabilities.
A R T I C L E I N F O
Keywords • Additive manufacturing, Fused deposition modeling (FDM), Dynamic modulus of elasticity, Fraction factorial design, Artificial neural network (ANN), Process parameters, Analysis of variance (ANOVA)
Corresponding author • Mohamed, Omar A.
Article history • Received 18 June 2016, First Revision 22 August 2016, Second Revision 28 August 2016, Accepted 29 August 2016
Published on-line • 9 September 2016
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