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Vijay.B. Nidagundi, R. Keshavamurthy, C. Prakash (2015)
Studies on Parametric Optimization for Fused Deposition Modelling ProcessMaterials Today: Proceedings, 2
D. Pramanik, A. Mandal, A. Kuar (2020)
An experimental investigation on improvement of surface roughness of ABS on fused deposition modelling processMaterials Today: Proceedings, 26
Che Wang, Ta‐Wei Lin, Shr‐Shiung Hu (2007)
Optimizing the rapid prototyping process by integrating the Taguchi method with the Gray relational analysisRapid Prototyping Journal, 13
Ismail Durgun, Rukiye Ertan (2014)
Experimental investigation of FDM process for improvement of mechanical properties and production costRapid Prototyping Journal, 20
G. Mavrotas (2009)
Effective implementation of the epsilon-constraint method in Multi-Objective Mathematical Programming problemsAppl. Math. Comput., 213
ARPN Journal of Engineering and Applied Sciences, 12
H. Shahabi, M. Ratnam (2010)
Noncontact roughness measurement of turned parts using machine visionThe International Journal of Advanced Manufacturing Technology, 46
ASTM and ISO (2015)
Standard terminology for additive manufacturing – general principles – terminology
O. Mohamed, S. Masood, J. Bhowmik (2016)
Mathematical modeling and FDM process parameters optimization using response surface methodology based on Q-optimal designApplied Mathematical Modelling, 40
O. Mohamed, S. Masood, J. Bhowmik (2015)
Optimization of fused deposition modeling process parameters: a review of current research and future prospectsAdvances in Manufacturing, 3
D. Correia, C. Gonçalves, S. Cunha, V. Ferraresi (2005)
Comparison between genetic algorithms and response surface methodology in GMAW welding optimizationJournal of Materials Processing Technology, 160
N. Johnson (1949)
Systems of frequency curves generated by methods of translation.Biometrika, 36 Pt. 1-2
Sandeep Rathee, Manu Srivastava, S. Maheshwari, A. Siddiquee (2017)
Effect of varying spatial orientations on build time requirements for FDM process: A case studyDefence Technology, 13
D. Frank, G. Fadel (1995)
Expert system-based selection of the preferred direction of build for rapid prototyping processesJournal of Intelligent Manufacturing, 6
Journal for Manufacturing Science and Production, 16
D. Dutta, F. Prinz, D. Rosen, L. Weiss (2001)
Layered Manufacturing: Current Status and Future TrendsJ. Comput. Inf. Sci. Eng., 1
K. Thrimurthulu, P. Pandey, N. Reddy (2004)
Optimum part deposition orientation in fused deposition modelingInternational Journal of Machine Tools & Manufacture, 44
S. Ford, L. Mortara, T. Minshall (2016)
The Emergence of Additive Manufacturing: Introduction to the Special IssueTechnological Forecasting and Social Change, 102
V. Canellidis, J. Giannatsis, V. Dedoussis (2009)
Genetic-algorithm-based multi-objective optimization of the build orientation in stereolithographyThe International Journal of Advanced Manufacturing Technology, 45
V. Kuznetsov, A. Solonin, Azamat Tavitov, Oleg Urzhumtsev, Anna Vakulik (2020)
Increasing strength of FFF three-dimensional printed parts by influencing on temperature-related parameters of the processRapid Prototyping Journal, 26
M. Raju, M. Gupta, Neeraj Bhanot, V. Sharma (2019)
A hybrid PSO–BFO evolutionary algorithm for optimization of fused deposition modelling process parametersJournal of Intelligent Manufacturing
S. Vyavahare, Shailendra Kumar, D. Panghal (2020)
Experimental study of surface roughness, dimensional accuracy and time of fabrication of parts produced by fused deposition modellingRapid Prototyping Journal, 26
P. Gurrala, S. Regalla (2011)
Optimization of Support Material and Build Time in Fused Deposition Modeling (FDM)Applied Mechanics and Materials, 110-116
The advancement of additive manufacturing technologies has resulted in producing parts of high quality and reduced manufacturing time. This paper aims to achieve a simultaneous optimal solution for build time and surface roughness as the output data and also to find the best values for the input data consisting of build orientation, extrusion width, layer thickness, infill percentage and raster angle.Design/methodology/approachFor this purpose, the effects of process parameters on the response variables were investigated by the design of experiments approach to develop empirical models using response surface methodology. The experimental parts of this research were conducted using an inexpensive and locally assembled fused filament fabrication (FFF) machine. A total of 50 runs for 4 different geometries, namely, cylinder, prism, 3DBenchy and twist gear vase, were performed using the rotatable central composite design, and each process parameters were investigated in two levels to develop empirical models. Also, a novel optimization method, namely, the posterior-based method, was accomplished to find the best values for the response variables.FindingsThe results demonstrated that not only the build orientation and layer thickness have notable effects on both response variables but also build time is dependent on extrusion width and infill percentage. Low infill percentage and high extrusion width resulted in increasing build time. By reducing layer thickness and infill percentage while increasing extrusion width, parts of high-quality surface finish and reduced built time were produced. Optimum process parameters were found to be of build direction of 0°, extrusion width of 0.61 mm, layer thickness of 0.22 mm, infill percentage of 20% and raster angle of 0°.Originality/valueThrough the developed empirical models and by minimizing build orientation and layer thickness, and also considerations for process parameters, parts of high-quality surface finish and reduced built time could be produced on FFF machines. To compensate for increased build time because of reduction in layer thickness, extrusion width and infill percentage must have their maximum and minimum value, respectively.
Rapid Prototyping Journal – Emerald Publishing
Published: Jul 14, 2021
Keywords: Surface roughness; Fused filament fabrication; Response surface methodology; Build orientation; Build time; Posterior-based method
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