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Surface Roughness Analysis, Modelling and Prediction in Fused Deposition Modelling Additive Manufacturing Technology

Authors: Yusuf S. Dambatta, Ahmed A. D. Sarhan


Fused deposition modelling (FDM) is one of the most prominent rapid prototyping (RP) technologies which is being used to efficiently fabricate CAD 3D geometric models. However, the process is coupled with many drawbacks, of which the surface quality of the manufactured RP parts is among. Hence, studies relating to improving the surface roughness have been a key issue in the field of RP research. In this work, a technique of modelling the surface roughness in FDM is presented. Using experimentally measured surface roughness response of the FDM parts, an ANFIS prediction model was developed to obtain the surface roughness in the FDM parts using the main critical process parameters that affects the surface quality. The ANFIS model was validated and compared with experimental test results.

Keywords: Orientation, Fused Deposition Modelling, ANFIS, surface roughness, adaptive neuro fuzzy inference system

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[1] Too, M., et al., Investigation of 3D non-random porous structures by fused deposition modelling. The International Journal of Advanced Manufacturing Technology, 2002. 19(3): p. 217-223.
[2] Atzeni, E., et al., Redesign and cost estimation of rapid manufactured plastic parts. Rapid Prototyping Journal, 2010. 16(5): p. 308-317.
[3] Karapatis, N., J. Van Griethuysen, and R. Glardon, Direct rapid tooling: a review of current research. Rapid Prototyping Journal, 1998. 4(2): p. 77-89.
[4] Huang, B., Alternate slicing and deposition strategies for Fused Deposition Modelling. 2014, Auckland University of Technology.
[5] Boschetto, A., V. Giordano, and F. Veniali, 3D roughness profile model in fused deposition modelling. Rapid Prototyping Journal, 2013. 19(4): p. 240-252.
[6] Garg, A., et al., A hybrid\ text {M} 5^\ prime-genetic programming approach for ensuring greater trustworthiness of prediction ability in modelling of FDM process. Journal of Intelligent Manufacturing, 2014. 25(6): p. 1349-1365.
[7] Anitha, R., S. Arunachalam, and P. Radhakrishnan, Critical parameters influencing the quality of prototypes in fused deposition modelling. Journal of Materials Processing Technology, 2001. 118(1): p. 385-388.
[8] Sun, Q., et al., Effect of processing conditions on the bonding quality of FDM polymer filaments. Rapid Prototyping Journal, 2008. 14(2): p. 72-80.
[9] Standard, A., F2792-12a.(2012).“. Standard Terminology for Additive Manufacturing Technologies,” ASTM International, West Conshohocken, Pa.
[10] Boschetto, A. and L. Bottini, Roughness prediction in coupled operations of fused deposition modeling and barrel finishing. Journal of Materials Processing Technology, 2015. 219: p. 181-192.
[11] Sarhan, A.A., et al., Geometrical Structure and Layer Orientation Effects on Strength, Material Consumption and Building Time of FDM Rapid Prototyped Samples. World Academy of Science, Engineering and Technology, International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering, 2015. 9(6): p. 1063-1068.
[12] 2008 (cited 2015 12/10/2015); Available from:
[13] Azar, A.T., Neuro-fuzzy applications in dialysis systems, in Modeling and Control of Dialysis Systems. 2013, Springer. p. 1223-1274.
[14] Jang, J.-S.R., ANFIS: adaptive-network-based fuzzy inference system. Systems, Man and Cybernetics, IEEE Transactions on, 1993. 23(3): p. 665-685.
[15] Soltan, I.M.A.-R., Surface Roughness Prediction in End-Milling Process. 2008.