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Materials 2016, 9(11), 895; doi:10.3390/ma9110895

Analytical Modelling and Optimization of the Temperature-Dependent Dynamic Mechanical Properties of Fused Deposition Fabricated Parts Made of PC-ABS

1
Department of Mechanical and Product Design Engineering, Swinburne University of Technology, Hawthorn 3122, Victoria, Australia
2
Department of Statistics, Data Science and Epidemiology, Swinburne University of Technology, Hawthorn 3122, Victoria, Australia
*
Author to whom correspondence should be addressed.
Academic Editor: Geminiano Mancusi
Received: 26 August 2016 / Revised: 23 October 2016 / Accepted: 31 October 2016 / Published: 4 November 2016
(This article belongs to the Section Manufacturing Processes and Systems)
View Full-Text   |   Download PDF [11478 KB, uploaded 4 November 2016]   |  

Abstract

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. View Full-Text
Keywords: fused deposition modeling (FDM); IV-Optimal response surface design; artificial neural network; process parameters; storage compliance; loss compliance; optimization fused deposition modeling (FDM); IV-Optimal response surface design; artificial neural network; process parameters; storage compliance; loss compliance; optimization
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Mohamed, O.A.; Masood, S.H.; Bhowmik, J.L. Analytical Modelling and Optimization of the Temperature-Dependent Dynamic Mechanical Properties of Fused Deposition Fabricated Parts Made of PC-ABS. Materials 2016, 9, 895.

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