Analytical modelling and optimization of the temperature-dependent dynamic mechanical properties of fused deposition fabricated parts made of PC-ABS

Mohamed, Omar Ahmed, Masood, Syed Hasan and Bhowmik, Jahar Lal 2016, Analytical modelling and optimization of the temperature-dependent dynamic mechanical properties of fused deposition fabricated parts made of PC-ABS, Materials, vol. 9, no. 11, doi: 10.3390/ma9110895.

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Title Analytical modelling and optimization of the temperature-dependent dynamic mechanical properties of fused deposition fabricated parts made of PC-ABS
Author(s) Mohamed, Omar Ahmed
Masood, Syed Hasan
Bhowmik, Jahar Lal
Journal name Materials
Volume number 9
Issue number 11
Total pages 19
Publisher MDPI
Place of publication Basel, Switzerland
Publication date 2016
ISSN 1996-1944
Keyword(s) IV-Optimal response surface design
artificial neural network
fused deposition modeling (FDM)
loss compliance
optimization
process parameters
storage compliance
Science & Technology
Technology
Materials Science, Multidisciplinary
Materials Science
FDM PROCESS
IMPACT
Summary Fused deposition modeling (FDM) additive manufacturing has been intensively used for many industrial applications due to its attractive advantages over traditional manufacturing processes. The process parameters used in FDM have significant influence on the part quality and its properties. This process produces the plastic part through complex mechanisms and it involves complex relationships between the manufacturing conditions and the quality of the processed part. In the present study, the influence of multi-level manufacturing parameters on the temperature-dependent dynamic mechanical properties of FDM processed parts was investigated using IV-optimality response surface methodology (RSM) and multilayer feed-forward neural networks (MFNNs). The process parameters considered for optimization and investigation are slice thickness, raster to raster air gap, deposition angle, part print direction, bead width, and number of perimeters. Storage compliance and loss compliance were considered as response variables. The effect of each process parameter was investigated using developed regression models and multiple regression analysis. The surface characteristics are studied using scanning electron microscope (SEM). Furthermore, performance of optimum conditions was determined and validated by conducting confirmation experiment. The comparison between the experimental values and the predicted values by IV-Optimal RSM and MFNN was conducted for each experimental run and results indicate that the MFNN provides better predictions than IV-Optimal RSM.
Language eng
DOI 10.3390/ma9110895
Indigenous content off
Field of Research 03 Chemical Sciences
09 Engineering
HERDC Research category C1.1 Refereed article in a scholarly journal
Copyright notice ©2016, The Authors
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30123033

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