Skip to main content
SpringerLink
Log in

Bayesian regularization-based Levenberg–Marquardt neural model combined with BFOA for improving surface finish of FDM processed part

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Fused deposition modeling has a complex part building mechanism making it difficult to obtain reasonably good functional relationship between responses and process parameters. To solve this problem, present study proposes use of artificial neural network (ANN) model to determine the relationship between five input parameters such as layer thickness, orientation, raster angle, raster width, and air gap with three output responses viz., roughness in top, bottom, and side surface of the built part. Bayesian regularization is adopted for selection of optimum network architecture because of its ability to fix number of network parameters irrespective of network size. ANN model is trained using Levenberg–Marquardt algorithm, and the resulting network has good generalization capability that eliminates the chance of over fitting. Finally, bacterial foraging optimization algorithm which attempts to model the individual and group behavior of Escherichia coli bacteria as a distributed optimization process is used to suggest theoretical combination of parameter settings to improve overall roughness of part. This paper also investigates use of chaotic time series sequence known as logistic function and demonstrates its superiority in terms of convergence and solution quality.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Luis Pérez CJ, Calvet JV, Sebastián Pérez MA (2001) Geometric roughness analysis in solid free form manufacturing process. J Mater Process Technol 119:52–57

    Article  Google Scholar 

  2. Voorakarnam V, Paul KB (2001) Effect of layer thickness and orientation angle on surface roughness in laminated object manufacturing. J Manuf Process 3(2):94–101

    Article  Google Scholar 

  3. Anitha R, Arunachalam S, Radhakrishnan P (2001) Critical parameters influencing the qualities of prototype in fused deposition modelling. J Mater Process Technol 118:385–388

    Article  Google Scholar 

  4. Campbell RI, Martorelli M, Lee HS (2002) Surface roughness visualization for rapid prototype models. Comput Aided Des 34:717–725

    Article  Google Scholar 

  5. Thrimurthulu K, Pandey PM, Reddy NV (2004) Optimum part deposition orientation in fused deposition modeling. Int J Mach Tools Manuf 44:585–594

    Article  Google Scholar 

  6. Kim HC, Lee SH (2005) Reduction of post processing for stereolithography systems by fabrication direction optimization. J Mech Sci Technol 37:711–725

    Google Scholar 

  7. Ahn D, Kweon JH, Kwon S, Song J, Lee S (2009) Representation of surface roughness in fused deposition modelling. J Mater Process Technol 209(15–16):5593–5600

    Article  Google Scholar 

  8. Sood AK, Ohdar RK, Mahapatra SS (2009) Improving dimensional accuracy of fused deposition modelling process using grey Taguchi method. Mater Des 30(10):4243–4252

    Article  Google Scholar 

  9. Sood AK, Ohdar RK, Mahapatra SS (2010) Parametric appraisal of mechanical property of fused deposition modelling processed parts. Mater Des 31(1):287–295

    Article  Google Scholar 

  10. Torrecilla JS, Otero L, Sanz PD (2007) Optimization of an artificial neural network for thermal/pressure food processing: evaluation of training algorithms. Comput Electron Agric 56(2):101–110

    Article  Google Scholar 

  11. Fadare DA, Ofidhe UI (2009) Artificial neural network model for prediction of friction factor in pipe flow. J Appl Sci Res 5(6):662–670

    Google Scholar 

  12. Debabrata M, Pal Surjya K, Partha S (2007) Modeling of electrical discharge machining process using back propagation neural network and multi-objective optimization using non-dominating sorting genetic algorithm-II. J Mater Process Technol 186:154–162

    Article  Google Scholar 

  13. Chun-Yao L, Yi-Xing S, Jung-Cheng C, Yi-Yin L, Chih-Wen C (2009) Neural networks and particle swarm optimization based MPPT for small wind power generator. World Acad Sci Eng Technol 60:17–23

    Google Scholar 

  14. Liu Y, Passino KM, Simaan MA (2002) Biomimicry of social foraging bacteria for distributed optimization: models, principles, and emergent behaviors. J Optim Theory Appl 115(3):603–628

    Article  MathSciNet  MATH  Google Scholar 

  15. Mahapatra SS, Kumar PS, Saumyakant P, Kumar SA (2009) Optimization of fused deposition modelling (FDM) process parameters using bacterial foraging technique. Intell Inf Manag 1:89–97

    Google Scholar 

  16. Goldberg DE (1989) Genetic algorithm in search, optimization and machine learning. Addison-Wesley Longman, Boston

    Google Scholar 

  17. Biswas S, Mahapatra SS (2009) An improved metaheuristic approach for solving the machine loading problem in flexible manufacturing systems. Int J Serv Oper Manag 5(1):76–93

    Google Scholar 

  18. Swagatam D, Sambarta D, Arijit B, Ajith A, Amit K (2009) On stability of the chemotactic dynamics in bacterial foraging optimization algorithm. IEEE Trans Syst Man Cyber Part A Syst Hum 39(3):670–679

    Article  Google Scholar 

  19. Min FL (1994) Neural network in computer intelligence. McGraw-Hill, New Delhi

    Google Scholar 

  20. Chidrawar K, Bhaskarwar S, Sujata M, Balasaheb P (2009) Implementation of neural network for generalized predictive control: a comparison between a Newton Raphson and Levenberg Marquardt implementation. 2009 World Congress on Comp Sci Inform England, pp 669–673

  21. Hirschen K, Schafer M (2006) Bayesian regularization neural networks for optimizing fluid flow processes. Comp Methods Appl Mech Eng 195:481–500

    Article  MATH  Google Scholar 

  22. Martin HT, Menhaj MB (1994) Training feed forward networks with the Marquardt algorithm. IEEE Trans Neural Netw 5(6):989–993

    Article  Google Scholar 

  23. Foresee FD, Hagan MT (1997) Gauss–Newton approximation to Bayesian learning. IEEE Trans Neural Netw 3:1930–1935

    Google Scholar 

  24. Furferi R, Governi L (2008) The recycling of wool clothes: an artificial neural network colour classification tool. Int J Adv Manuf Tech 37(7–8):722–731

    Article  Google Scholar 

  25. Basterrech S, Mohammed S, Rubino G, Soliman M (2011) Levenberg–Marquardt training algorithms for random neural networks. The Comput J 54(1):125–135

    Article  Google Scholar 

  26. Tambourgi EB, Fischer GA, Fileti AMF (2006) Neural modeling for cytochrome b5 extraction. Proc Biochem 41(6):1272–1276

    Article  Google Scholar 

  27. Ciurana J, Arias G, Ozel T (2009) Neural network modeling and particle swarm optimization (PSO) of process parameters in pulsed laser micromachining of hardened AISI H13 steel. Mater Manuf Proc 24(3):358–368

    Article  Google Scholar 

  28. Howard D, Martin H, Beale M (2010) Neural network Toolbox™ user’s guide. The Maths Works, Natick

    Google Scholar 

  29. Montgomery DC (2003) Design and analysis of experiments, 5th edn. Wiley, Singapore

    Google Scholar 

  30. Passino KM (2002) Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Sys Mag 22:52–67

    Article  Google Scholar 

  31. Riccardo C, Fortuna L, Stefano F, Gabriella XM (2003) Chaotic sequences to improve the performance of evolutionary algorithms. IEEE Trans Evol Comput 7(3):289–304

    Article  Google Scholar 

  32. Nitesh K, Anoop P, Ravi S, Tiwari MK (2008) Fast clonal algorithm. Eng Appl Artif Intell 21:106–128

    Article  Google Scholar 

  33. Sood AK, Mahapatra SS, Ohdar RK (2011) Weighted principal component approach for improving surface finish of ABS plastic parts built through fused deposition modelling process. Int J Rapid Manuf 2(1/2):4–27

    Article  Google Scholar 

Download references

Acknowledgments

The authors express hearty thanks to the Editor-in-Chief of International Journal of advanced Manufacturing Technology and learned reviewers for their useful suggestions that helped to improve the literal and technical content of the paper.

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, National Institute of Technology, Rourkela, 769008, India

    S. S. Mahapatra

  2. Department of Manufacturing Engineering, National Institute of Foundry and Forge Technology, Ranchi, 834003, India

    Anoop Kumar Sood

Authors
  1. S. S. Mahapatra
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anoop Kumar Sood
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S. S. Mahapatra.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Mahapatra, S.S., Sood, A.K. Bayesian regularization-based Levenberg–Marquardt neural model combined with BFOA for improving surface finish of FDM processed part. Int J Adv Manuf Technol 60, 1223–1235 (2012). https://doi.org/10.1007/s00170-011-3675-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-011-3675-x

Keywords

Search

Navigation