IIM  Vol.1 No.2 , November 2009
Optimization of Fused Deposition Modelling (FDM) Process Parameters Using Bacterial Foraging Technique
ABSTRACT
Fused deposition modelling (FDM) is a fast growing rapid prototyping (RP) technology due to its ability to build functional parts having complex geometrical shapes in reasonable build time. The dimensional accuracy, surface roughness, mechanical strength and above all functionality of built parts are dependent on many process variables and their settings. In this study, five important process parameters such as layer thickness, orientation, raster angle, raster width and air gap have been considered to study their effects on three responses viz., tensile, flexural and impact strength of test specimen. Experiments have been conducted using central composite design (CCD) and empirical models relating each response and process parameters have been developed. The models are validated using analysis of variance (ANOVA). Finally, bacterial foraging technique is used to suggest theoretical combination of parameter settings to achieve good strength simultaneously for all responses.

Cite this paper
nullS. PANDA, S. PADHEE, A. SOOD and S. MAHAPATRA, "Optimization of Fused Deposition Modelling (FDM) Process Parameters Using Bacterial Foraging Technique," Intelligent Information Management, Vol. 1 No. 2, 2009, pp. 89-97. doi: 10.4236/iim.2009.12014.
References
[1]   B. Wiedemann and H. A. Jantzen, “Strategies and applications for rapid product and process development in Daimler-Benz AG. Computers in Industries,” Vol. 39, No. 1, pp. 11–25, 1999.

[2]   S. Upcraft and R. Fletcher, “The rapid prototyping technologies”, Rapid Prototyping Journal, Vol. 23, No. 4, pp. 318–330, 2003.

[3]   S. Mansour and R. Hauge, “Impact of rapid manufacturing on design for manufacturing for injection moulding,” Journal of Engineering Manufacture, Part B, Vol. 217, No. 4, pp. 453–461, 2003.

[4]   N. Hopkinson, R. J. M. Hagur, and P. H.Dickens, “Rapid manufacturing: An industrial revolution for the digital age,” John Wiley and Sons Ltd., England, 2006.

[5]   A. Bernarand and A. Fischer, “New trends in rapid product development,” CIRP Annals-Manufacturing Technology, Vol. 51, No. 2, pp. 635–652, 2002.

[6]   G. N. Levy, R. Schindel, J. P. Kruth, and K. U. Leuven, “Rapid manufacturing and rapid tooling with layer manufacturing (LM) technologies-State of the art and future perspectives,” CIRP Annals-Manufacturing Te- chnologies, Vol. 52, No. 2, pp. 589–609, 2003.

[7]   A. Pilipovi?, P. Raos, M. ?ercer, “Experimental analysis of properties of materials for rapid prototyping,” International Journal of Advanced Manufacturing Technology, Vol. 40, No. 11–12, pp. 105–115, 2009.

[8]   P. M. Pandey, P. K. Jain, and P. V. M.Rao, “Effect of delay time on part strength in selective laser Sintering,” International Journal of Advanced Manufacturing Te- chnology, Vol. 43, No. 1–2, pp. 117–126, 2009.

[9]   K. Chockalingama, N. Jawahara, and U. Chandrasekhar, “Influence of layer thickness on mechanical properties in stereolithography,” Rapid Protyping Journal, Vol. 12, No. 6, pp. 106–113, 2006.

[10]   Y. Liu, K. M. Passino, and M. A. Simaan, “Biomimicry of social foraging bacteria for distributed optimization: Models, principles, and emergent behaviors,” Journal of Optimization Theory and Applications, Vol. 115, No. 3, pp. 603–628, 2002.

[11]   D. E. Goldberg, “Genetic algorithm in search, optimization and machine learning,” Addison-Wesley Longman Publishing Co., Inc. Boston, MA, 1989.

[12]   S. Biswas and S. S. Mahapatra, “An improved metaheuristic approach for solving the machine loading problem in flexible manufacturing systems,” International Journal of Services and Operations Management, Vol. 5, No. 1, pp. 76–93, 2009.

[13]   S. H. Ahn, M. Montero, D. Odell, S. Roundy, and P. K. Wright, “Anisotropic material properties of fused deposition modelling ABS,” Rapid Prototyping Journal, Vol. 8, No. 4, pp. 248–257, 2002.

[14]   Khan, Z.A., Lee, B.H., and J. Abdullah, “Optimization of rapid prototyping parameters for production of flexible ABS object,” Journal of Material Processing Technology, Vol. 169, pp. 54–61, 2005.

[15]   C. S. Lee, S. G. Kim, H. J. Kim, and S. H. Ahn, “Measurement of anisotropic compressive strength of rapid prototyping parts,” Journal of Material Processing Technology, Vol. 187–188, pp. 637–630, 2007.

[16]   T. M. Wang, J. T. Xi, and Y. Jin, “A model research for prototype warp deformation in the FDM process,” International Journal of Advanced Manufacturing Technology, Vol. 33, No. 11–12, pp. 1087–1096, 2007.

[17]   C. T. Bellehumeur, P. Gu, Q. Sun, and G. M. Rizvi, “Effect of processing conditions on the bonding quality of FDM polymer filaments,” Rapid Prototyping Journal, Vol. 14, No. 2, pp. 72–80, 2008.

[18]   K. Chou and Y. Zhang, “A parametric study of part distortion in fused deposition modeling using three dimensional element analysis,” Journal of Engineering Manufacture, Part B, Vol. 222, pp. 959–967, 2008.

[19]   E. Mezura-Montes and C. A. Coello Coello, “Constrained optimization via Multiobjective Evolutionary Algorithms,” In J. Knowles, D. Corne, and K. Deb, editors, Multiobjective Problem Solving from Nature, Springer, Heidelberg, pp. 53–75, 2008.

[20]   Z. Michalewicz and M. Schoenauer, “Evolutionary algorithms for constrained parameter optimization problems,” Evolutionary Computation, Vol. 4, No. 1, pp. 1–32, 1996.

[21]   S. He, E. Prempain, and Q. H. Wu, “An improved particle swarm optimizer for mechanical design optimization problems,” Engineering Optimization, Vol. 36, No. 5, pp. 585–605, 2004.

[22]   T. Ray and K. Liew, “Society and civilization: an optimization algorithm based on the simulation of social behavior,” IEEE Transactions on Evolutionary Computation, Vol. 7, No. 4, pp. 386–396, 2003.

[23]   J. Kennedy and R. C. Eberhart, “Swarm intelligence,” Morgan Kaufmann, UK, 2001.

[24]   G. Leguizamón and C. Coello-Coello, “A boundary search based ACO algorithm coupled with stochastic ranking,” In 2007 IEEE Congress on Evolutionary Computation (CEC’07), Singapore, September 2007. IEEE Press, pp. 165–172, 2007.

[25]   D. Karaboga and B. Basturk, “On the performance of artificial bee colony (ABC) algorithm,” Applied Soft Computing, Vol. 8, No. 1, pp. 687–697, 2008.

[26]   K. Passino, “Biomimicry of bacterial foraging for distributed optimization and control,” IEEE Control Systems Magazine, Vol. 22, No. 3, pp. 52–67, 2002.

[27]   R. Majhi, G. Panda, G. Sahoo, P. Dash, and D. Das, “Stock market prediction of s&p 500 and DJIA using bacterial foraging optimization technique,” In Proceedings of the IEEE Congress on Evolutionary Computation (CEC’07), Singapore, IEEE Service Center, pp. 2569-2575, September 2007.

[28]   S. Mishra, G. D. Reddy, P. E. Rao, and K. Santosh, “Implementation of new evolutionary techniques for transmission loss reduction,” In Proceedings of the IEEE Congress on Evolutionary Computation (CEC’ 07), Singapore, IEEE Service Center, pp. 2331–2336, September 2007.

[29]   B. Majhi and G. Panda, “Bacteria foraging based identificacion of nonlinear dynamic system,” In Proceedings of the IEEE Congress on Evolutionary Computation (CEC’07), Singapore, IEEE Service Center, pp. 1636–1641, September 2007.

[30]   D. C. Montgomery, “Design and Analysis of Experiments”, Fifth Edition, John Wiley and Sons Pvt. Ltd., Singapore, 2003.

 
 
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