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Application of an RBF neural network for FDM parts’ surface roughness prediction for enhancing surface quality

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Abstract

To improve the surface roughness of parts fabricated using fused deposition modeling, modeling of the surface roughness distribution is used before the fabrication process to enable more precise planning of the additive manufacturing process. In this paper, a new methodology based on radial basis function neural networks (RBFNNs) is proposed for estimation of the surface roughness based on experimental results. The effective variables of the RBFNN are optimized using the imperialist competitive algorithm (ICA). The RBFNN-ICA model outperforms considerably comparing to the RBFNN model. A specific test part capable of evaluating the surface roughness distribution for varied surface build angles is built. To demonstrate the advantage of the recommended model, a performance comparison of the most well-known analytical models is carried out. The results of the evaluation confirm the capability of more fitted responses in the proposed modeling. The RBFNN and RBFNN-ICA models have mean absolute percentage error of 7.11% and 3.64%, respectively, and mean squared error of 7.48 and 2.27, respectively. The robustness of the network is studied based on the RBFNN’s effective variables evaluation and sensitivity analysis assessment for the contribution of input parameters. Finally, the comprehensive validity assessments confirm improved results using the recommended modeling.

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References

  1. Bernard, A. and Fischer, A., “New Trends in Rapid Product Development,” CIRP Annals-Manufacturing Technology, Vol. 51, No. 2, pp. 635–652, 2002.

    Article  Google Scholar 

  2. Hopkinson, N., Hague, R., and Dickens, P., “Rapid Manufacturing: An Industrial Revolution for the Digital Age,” John Wiley & Sons, 2006.

    Google Scholar 

  3. Upcraft, S. and Fletcher, R., “The Rapid Prototyping Technologies,” Assembly Automation, Vol. 23, No. 4, pp. 318–330, 2003.

    Article  Google Scholar 

  4. Mansour, S. and Hague, R., “Impact of Rapid Manufacturing on Design for Manufacture for Injection Moulding,” Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, Vol. 217, No. 4, pp. 453–461, 2003.

    Article  Google Scholar 

  5. Pham, D. T. and Gault, R. S., “A Comparison of Rapid Prototyping Technologies,” International Journal of Machine Tools and Manufacture, Vol. 38, No. 10, pp. 1257–1287, 1998.

    Article  Google Scholar 

  6. Wiedemann, B. and Jantzen, H.-A., “Strategies and Applications for Rapid Product and Process Development in Daimler-Benz AG,” Computers in Industry, Vol. 39, No. 1, pp. 11–25, 1999.

    Article  Google Scholar 

  7. Yan, X. and Gu, P., “A Review of Rapid Prototyping Technologies and Systems,” Computer-Aided Design, Vol. 28, No. 4, pp. 307–318, 1996.

    Article  Google Scholar 

  8. Onuh, S. O. and Yusuf, Y. Y., “Rapid Prototyping Technology: Applications and Benefits for Rapid Product Development,” Journal of Intelligent Manufacturing, Vol. 10, No. 3-4, pp. 301–311, 1999.

    Article  Google Scholar 

  9. Pandey, P. M., Reddy, N. V., and Dhande, S. G., “Slicing Procedures in Layered Manufacturing: A Review,” Rapid Prototyping Journal, Vol. 9, No. 5, pp. 274–288, 2003.

    Article  Google Scholar 

  10. Sood, A. K., Equbal, A., Toppo, V., Ohdar, R. K., and Mahapatra, S. S., “An Investigation on Sliding Wear of FDM Built Parts,” CIRP Journal of Manufacturing Science and Technology, Vol. 5, No. 1, pp. 48–54, 2012.

    Article  Google Scholar 

  11. Ahn, D., Kweon, J.-H., Kwon, S., Song, J., and Lee, S., “Representation of Surface Roughness in Fused Deposition Modeling,” Journal of Materials Processing Technology, Vol. 209, No. 15, pp. 5593–5600, 2009.

    Article  Google Scholar 

  12. Li, A., Zhang, Z., Wang, D., and Yang, J., “Optimization Method to Fabrication Orientation of Parts in Fused Deposition Modeling Rapid Prototyping,” Proc. of International Conference on Mechanic Automation and Control Engineering (MACE), pp. 416–419, 2010.

    Google Scholar 

  13. Byun, H. S. and Lee, K. H., “Determination of Optimal Build Direction in Rapid Prototyping with Variable Slicing,” The International Journal of Advanced Manufacturing Technology, Vol. 28, No. 3-4, pp. 307–313, 2006.

    Article  Google Scholar 

  14. Chang, D.-Y. and Huang, B.-H., “Studies on Profile Error and Extruding Aperture for the RP Parts using the Fused Deposition Modeling Process,” The International Journal of Advanced Manufacturing Technology, Vol. 53, No. 9-12, pp. 1027–1037, 2011.

    Article  Google Scholar 

  15. Thrimurthulu, K., Pandey, P. M., and Reddy, N. V., “Optimum Part Deposition Orientation in Fused Deposition Modeling,” International Journal of Machine Tools and Manufacture, Vol. 44, No. 6, pp. 585–594, 2004.

    Article  MATH  Google Scholar 

  16. Armillotta, A., “Assessment of surface Quality on Textured FDM Prototypes,” Rapid Prototyping Journal, Vol. 12, No. 1, pp. 35–41, 2006.

    Article  Google Scholar 

  17. Pandey, P. M., Reddy, N. V., and Dhande, S. G., “Real Time Adaptive Slicing for Fused Deposition Modelling,” International Journal of Machine Tools and Manufacture, Vol. 43, No. 1, pp. 61–71, 2003.

    Article  Google Scholar 

  18. Pandey, P. M., Reddy, N. V., and Dhande, S. G., “Improvement of Surface Finish by Staircase Machining in Fused Deposition Modeling,” Journal of Materials Processing Technology, Vol. 132, No. 1, pp. 323–331, 2003.

    Article  Google Scholar 

  19. Campbell, R. I., Martorelli, M., and Lee, H. S., “Surface Roughness Visualisation for Rapid Prototyping Models,” Computer-Aided Design, Vol. 34, No. 10, pp. 717–725, 2002.

    Article  Google Scholar 

  20. Reeves, P. E. and Cobb, R. C., “Surface Deviation Modelling of LTM Processes -A Comparative Analysis,” Proc. of 5th European Conference on Rapid Prototyping and Manufacturing, 1995.

    Google Scholar 

  21. Ahn, D., Kim, H., and Lee, S., “Surface Roughness Prediction Using Measured Data and Interpolation in Layered Manufacturing,” Journal of Materials Processing Technology, Vol. 209, No. 2, pp. 664–671, 2009.

    Article  Google Scholar 

  22. Perez, C. L., Vivancos, J., and Sebastian, M. A., “Surface Roughness Analysis in Layered Forming Processes,” Precision Engineering, Vol. 25, No. 1, pp. 1–12, 2001.

    Article  Google Scholar 

  23. Bordoni, M. and Boschetto, A., “Thickening of Surfaces for Direct Additive Manufacturing Fabrication,” Rapid Prototyping Journal, Vol. 18, No. 4, pp. 308–318, 2012.

    Article  Google Scholar 

  24. Ahn, D., Kim, H., and Lee, S., “Fabrication Direction Optimization to Minimize Post-Machining in Layered Manufacturing,” International Journal of Machine Tools and Manufacture, Vol. 47, No. 3, pp. 593–606, 2007.

    Article  MathSciNet  Google Scholar 

  25. Boschetto, A., Giordano, V., and Veniali, F., “Surface Roughness Prediction in Fused Deposition Modelling by Neural Networks,” The International Journal of Advanced Manufacturing Technology, Vol. 67, No. 9-12, pp. 2727–2742, 2013.

    Article  Google Scholar 

  26. Ahn, D.-K., Kwon, S.-M., and Lee, S.-H., “Expression for Surface Roughness Distribution of FDM Processed Parts,” Proc. of International Conference on Smart Manufacturing Application, pp. 490–493, 2008.

    Google Scholar 

  27. Mason, A., “Multi-Axis Hybrid Rapid Prototyping using Fusion Deposition Modeling,” M.Sc. Thesis, Department of Mechanical Engineering, Ryerson University, 2006.

    Google Scholar 

  28. Nourghassemi, B., “Surface Roughness Estimation for FDM Systems,” M.Sc. Thesis, Department of Mechanical Engineering, Ryerson University, 2011.

    Google Scholar 

  29. Reeves, P. E. and Cobb, R. C., “Reducing the Surface Deviation of Stereolithography Using in-Process Techniques,” Rapid Prototyping Journal, Vol. 3, No. 1, pp. 20–31, 1997.

    Article  Google Scholar 

  30. Rahmati, S. and Vahabli, E., “Evaluation of Analytical Modeling for Improvement of Surface Roughness of FDM Test Part using Measurement Results,” The International Journal of Advanced Manufacturing Technology, Vol. 79, No. 5-8, pp. 823–829, 2015.

    Article  Google Scholar 

  31. Haykin, S. S., Haykin, S. S., Haykin, S. S., and Haykin, S. S., “Neural Networks and Learning Machines,” Pearson Upper Saddle River, 2009.

    Google Scholar 

  32. Jain, A. K., Mao, J., and Mohiuddin, K. M., “Artificial Neural Networks: A Tutorial,” IEEE Computer, Vol. 29, No. 3, pp. 31–44, 1996.

    Article  Google Scholar 

  33. Foody, G. M., “Supervised Image Classification by MLP and RBF Neural Networks with and without an Exhaustively Defined Set of Classes,” International Journal of Remote Sensing, Vol. 25, No. 15, pp. 3091–3104, 2004.

    Article  Google Scholar 

  34. Assi, A. H., “Engineering Education and Research using Matlab,” InTech, 2011.

    Google Scholar 

  35. Du, K.-L. and Swamy, M. N., “Neural Networks in a Softcomputing Framework,” Springer Science & Business Media, 2006.

    Google Scholar 

  36. Beale, M. H., Hagan, M. T., and Demuth, H. B., “Neural Netword Toolbox User Guide,” https://www.mathworks.com/help/pdf_doc/ nnet/nnet_ug.pdf (Accessed 10 NOV 2016)

    Google Scholar 

  37. Atashpaz-Gargari, E. and Lucas, C., “Imperialist Competitive Algorithm: An Algorithm for Optimization Inspired by Imperialistic Competition,” Proc. of IEEE Congress on Evolutionary Computation, pp. 4661–4667, 2007.

    Google Scholar 

  38. Pandey, P. M., Thrimurthulu, K., and Reddy, N. V., “Optimal Part Deposition Orientation in FDM by using a Multicriteria Genetic Algorithm,” International Journal of Production Research, Vol. 42, No. 19, pp. 4069–4089, 2004.

    Article  MATH  Google Scholar 

  39. Sreedhar, P., MathikumarManikandan, C., and Jothi, G., “Experimental Investigation of Surface Roughness for Fused Deposition Modeled Part with Different Angular Orientation,” Technology, Vol. 5, No. 3, pp. 21–28, 2012.

    Google Scholar 

  40. Garson, D. G., “Interpreting Neural Network Connection Weights,” AI Expert, Vol. 6, No. 7, pp. 47–51, 1991.

    Google Scholar 

  41. Goh, A. T. C., “Back-Propagation Neural Networks for Modeling Complex Systems,” Artificial Intelligence in Engineering, Vol. 9, No. 3, pp. 143–151, 1995.

    Article  Google Scholar 

  42. Huang, G.-B., Saratchandran, P., and Sundararajan, N., “A Generalized Growing and Pruning RBF (GGAP-RBF) Neural Network for Function Approximation,” IEEE Transactions on Neural Networks, Vol. 16, No. 1, pp. 57–67, 2005.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Mechanical and Aerospace Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Ebrahim Vahabli & Sadegh Rahmati

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  1. Ebrahim Vahabli
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  2. Sadegh Rahmati
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Correspondence to Sadegh Rahmati.

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Vahabli, E., Rahmati, S. Application of an RBF neural network for FDM parts’ surface roughness prediction for enhancing surface quality. Int. J. Precis. Eng. Manuf. 17, 1589–1603 (2016). https://doi.org/10.1007/s12541-016-0185-7

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  • DOI: https://doi.org/10.1007/s12541-016-0185-7

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