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Surface roughness prediction in fused deposition modelling by neural networks

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Abstract

Fused deposition modelling is a proven technology for the fabrication of both aesthetic and functional prototypes. The obtainable roughness is the most limiting aspect for its application. The prediction of surface quality is an essential tool for the diffusion of this technology, in fact at product development stage, it allows to comply with design specifications and in process planning it is useful to determine manufacturing strategies. The existing models are not robust enough in predicting roughness parameters for all deposition angles, in particular for near horizontal walls. The aim of this work is to determine roughness parameters models reliable over the entire part surface. This purpose is pursued using a feed-forward neural network to fit experimental data. By the utilisation of an evaluation function, the best solution has been found. This has been obtained using a feed-forward neural network for fitting the experimental data. The best solution has been founded by using an evaluation function that we constructed. The validation proved the robustness of the model found to process parameters’ variation and its applicability to different FDM machines and materials.

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References

  1. Gebhardt A (2003) Rapid prototyping. Hanser, Munich

    Book  Google Scholar 

  2. Chua CK, Leong KF, Lim CS (2010) Rapid prototyping: principles and applications, 3rd edn. World Scientific, Singapore

    Book  Google Scholar 

  3. Wohlers TT, (2008) Wohlers Report 2008: Executive Summary. Colorado, USA

  4. Armillotta A (2006) Assessment of surface quality on textured FDM prototypes. Rapid Prototyp J 12:35–41

    Article  Google Scholar 

  5. Perez CJL, Vivancos J, Sebastiàn MA (2001) Surface roughness analysis in layered forming processes. Precis Eng 25:1–12

    Article  Google Scholar 

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

    Article  Google Scholar 

  7. Reeves PE, Cobb RC, (1995) Surface deviation modelling of LTM processes—a comparative analysis. In: Proceedings of the Fifth European Conference on Rapid Prototyping and Manufacturing, University of Nottingham, Nottingham

  8. Ahn D, Kim H, Lee S (2009) Surface roughness prediction using measured data and interpolation in layered manufacturing. J Mater Process Technol 209:664–671

    Article  Google Scholar 

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

    Article  Google Scholar 

  10. Pandey PM, Reddy NV, Dhande SG (2003) Real time adaptive slicing for fused deposition modeling. Int J Mach Tools Manuf 43:61–71

    Article  Google Scholar 

  11. Boschetto A, Giordano V, Veniali F (2011) Modelling micro geometrical profile in fused deposition process. Int J Adv Manuf Technol. doi:10.1007/s00170-011-3744-1

  12. Kohonen T (1988) An introduction to neural computing. Neural Netw 1:3–16

    Article  Google Scholar 

  13. Kohli A, Dixit US (2005) A neural-network-based methodology for the prediction of surface roughness in a turning process. Int J Adv Manuf Technol. doi:10.1007/s00170-003-1810-z

  14. Lee SS, Chen JC (2003) On-line surface roughness system using artificial neural networks system in turning operations. Int J Adv Manuf Technol. doi:10.1007/s00170-002-1511-z

  15. Karayel D (2008) Prediction and control of surface roughness in CNC lathe using artificial neural network. J Mater Process Technol 209:3125–3137

    Article  Google Scholar 

  16. Yang SH, Natarajan U, Sekar M, Palani S (2010) Prediction of surface roughness in turning operations by computer vision using neural network trained by differential evolution algorithm. Int J Adv Manuf Technol. doi:10.1007/s00170-010-2668-5

  17. Sonar DK, Dixit US, Ojha DK (2006) The application of radial basis function neural network for predicting the surface roughness in a turning process. Int J Adv Manuf Technol. doi:10.1007/s00170-004-2258-5

  18. Coit DW, Jackson BT, Smith AE (1998) Static neural network process models: considerations and case studies. Int J Prod Res 36:2953–2967

    Article  MATH  Google Scholar 

  19. EL-Mounayri H, Briceno JF (2010) A new artificial network approach to modeling ball-end milling. Int J Adv Manuf Technol. doi:10.1007/s00170-009-2217-2

  20. Muñoz-Escalona P, Maropoulos PG (2010) Artificial neural networks for surface roughness prediction when face milling AI 7075-t7351. J Mater Eng Perform. doi:10.1007/s1665-009-9452-4

  21. Balic J (2004) Neural-network based numerical control milling machine. J Intell Robot Syst 40:343–358

    Article  Google Scholar 

  22. Ozcelik B, Oktem H, Kurtarn H (2005) Optimum surface roughness in end milling inconel 718 by coupling neural network model and genetic algorithm. Int J Adv Manuf Technol. doi:10.1007/s00170-004-2175-7

  23. Oktem H, Erzurumlu T, Erzinznli F (2006) Prediction of minimum surface roughness in end milling mold parts using neural and genetic algorithm. Mater Design 27:735–744

    Article  Google Scholar 

  24. Benardos PG, Vosniakos GC (2002) Prediction of surface roughness in CNC face milling using neural networks and Taguchi’s design of experiments. Robot Comput Integr Manuf 18:343–354

    Article  Google Scholar 

  25. Quintana G, Garcia-Romeu ML, Ciurana J (2011) Surface roughness monitoring application based on artificial neural networks for ball-end milling operations. J Intell Manuf. doi:10.1007/s1084-009-0323-5

  26. Sanjay C, Jyothi C (2006) A study of surface roughness in drilling using mathematical analysis and neural networks. Int J Adv Manuf Technol. doi:10.1007/s00170-005-2538-8

  27. Markopoulos AP, Manolakos DE, Vaxevanidis NM (2008) Artificial neural network models for the prediction of surface roughness in electrical discharge machining. Int J Adv Manuf Technol. doi:10.1007/s10845-008-0081-9

  28. Mahapatra SS, Sood AK (2012) 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

    Article  Google Scholar 

  29. Huang SH, Zhang HC (1995) Neural-expert hybrid approach for intelligent manufacturing: a survey. Comput Ind 26:107–126

    Article  Google Scholar 

  30. Hayken S (1999) Neural networks: a comprehensive foundation. Prentice-Hall, Englewood Cliffs

    Google Scholar 

  31. Gareta R, Romeo LM, Gil A (2006) Forecasting of electricity prices with neural Networks. Energy Convers Manag 47:1770–1778

    Article  Google Scholar 

  32. Karatas C, Sozen A, Dulek E (2009) Modelling of residual stresses in the shot peened material C-1020 by artificial neural network. Expert Syst Appl 36:3514–3521

    Article  Google Scholar 

  33. Asiltürk I, Çunkas M (2011) Modeling and prediction of surface roughness in turning operations using artificial neural network and multiple regression method. Expert Syst Appl 38:5826–5832

    Article  Google Scholar 

  34. Pala M, Caglar N, Elmas M, Cevik A, Saribiyik M (2008) Dynamic soil structure interaction analysis of neural network. Constr Build Mater 22:330–342

    Article  Google Scholar 

  35. Lawrence J (1994) Introduction to neural networks: design, theory and applications, 6th edn. California Scientific Software, Nevada City

    Google Scholar 

  36. Haykin S (1999) Neural networks: a comprehensive foundation, 2nd edn. Prentice-Hall, Englewood Cliffs

    MATH  Google Scholar 

  37. Leong KF, Chua CK (1996) A study of stereolithography file errors and repair, part 1—generic solutions. Int J Adv Manuf Technol 12:407–414

    Article  Google Scholar 

  38. Leong KF, Chua CK (1996) A study of stereolithography file errors and repair, part 2—special cases. Int J Adv Manuf Technol 12:415–422

    Article  Google Scholar 

  39. Bordoni M, Boschetto A (2012) Thickening of surfaces for direct additive manufacturing fabrication. Rapid Prototyp J 18:308–318

    Article  Google Scholar 

  40. ISO/DTS 16610-22: 2006 Geometrical product specifications (GPS)—filtration—linear profile filters: Spline filters, pp. 1–6

  41. Whitehouse DJ (1994) Handbook of surface metrology. IOP Publishing Ltd., Bristol

    Google Scholar 

  42. Cybenko G (1989) Approximation by superpositions of a sigmoidal function. Math Control Signals Syst 2:303–314

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

  44. Principe JC, Euliano NR, Lefebvre WC, (2000) Neural and adaptive systems: fundamentals through simulations. Wiley, New York

  45. ISO 4287:1997. Geometrical product specifications (GPS)—surface texture: profile method—terms, definitions and surface texture parameters, pp. 1–25

  46. Costa A (1984) Examples of a complete minimal immersion in R 3 of genus one and three embedded ends. Bil Soc Bras Mat 15:47–54

    Article  MATH  Google Scholar 

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Authors and Affiliations

  1. Dipartimento di Ingegneria Meccanica e Aerospaziale, Sapienza Università di Roma, 00184, Rome, Italy

    A. Boschetto, V. Giordano & F. Veniali

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  1. A. Boschetto
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  2. V. Giordano
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  3. F. Veniali
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Correspondence to V. Giordano.

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Boschetto, A., Giordano, V. & Veniali, F. Surface roughness prediction in fused deposition modelling by neural networks. Int J Adv Manuf Technol 67, 2727–2742 (2013). https://doi.org/10.1007/s00170-012-4687-x

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  • DOI: https://doi.org/10.1007/s00170-012-4687-x

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