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Comparing statistical models and artificial neural networks on predicting the tool wear in hard machining D2 AISI steel

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Abstract

The current article presents an investigation into predicting tool wear in hard machining D2 AISI steel using neural networks. An experimental investigation was carried out using ceramic cutting tools, composed approximately of Al2O3 (70%) and TiC (30%), on cold work tool steel D2 (AISI) heat treated to a hardness of 60 HRC. Two models were adjusted to predict tool wear for different values of cutting speed, feed and time, one of them based on statistical regression, and the other based on a multilayer perceptron neural network. Parameters of the design and the training process, for the neural network, have been optimised using the Taguchi method. Outcomes from the two models were analysed and compared. The neural network model has shown better capability to make accurate predictions of tool wear under the conditions studied.

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References

  1. Obikawa T, Matsumura T, Shirakashi T, Usui E (1997) Wear characteristics of alumina coated and alumina ceramic tools. J Mater Process Technol 63(1–3):211–216

    Article  Google Scholar 

  2. D’Errico GE, Calzavarini R, Chiara R, Morrell R, Lay L (1995) Performance of ceramic cutting tools in turning operations. In: Ceramics: Charting the future. Techna Srl, pp 2327–2334

  3. Xu C, Huang C, Ai X (2001) Mechanical property and cutting performance of yttrium-reinforced Al203/Ti(C,N) composite ceramic tool material. J Mater Eng Perform 10(1):102–107

    Article  Google Scholar 

  4. Barry J, Bryne G (2001) Cutting tool wear in the machining of hardened steels. Part I: Alumina/TiC cutting tool wear. Wear 247(2):139–151

    Article  Google Scholar 

  5. Barry J, Bryne G (2001) Cutting tool wear in the machining of hardened steels. Part II: Cubic boron nitride cutting tool wear. Wear 247(2):152–160

    Article  Google Scholar 

  6. Chou YK, Song H (2005) Thermal modelling for white layer predictions in finish hard turning. Int J Mach Tools Manuf 45(4–5):481–495

    Article  Google Scholar 

  7. Grzesik W, Wanat T (2005) Comparative assessment of surface roughness produced by hard machining with mixed ceramic tools including 2D and 3D analysis. J Mater Proces Technol 169(3):364–371

    Article  Google Scholar 

  8. Paulo Davim J, Figueira L (2007) Machinability evaluation in hard turning of cold work tool steel (D2) with ceramic tools using statistical techniques. Mater Design 28(4):1186–1191

    Article  Google Scholar 

  9. Lima JG, Ávila RF, Abrão AM, Faustino M, Paulo Davim J (2005) Hard turning: AISI 4340 high strength alloy steel and AISI D2 cold work tool steel. J Mater Proces Technol 169(3):388–395

    Article  Google Scholar 

  10. Mukherjee I, Ray PK (2006) A review of optimization techniques in metal cutting processes. Comput Ind Eng 50(1):15–34

    Article  Google Scholar 

  11. Taylor FW (1907) On the art of cutting metals. Trans ASME 28:310–350

    Google Scholar 

  12. Liu XL, Wen DH, Li ZJ, Xiao L, Yan FG (2002) Cutting temperature and tool wear of hard turning hardened bearing steel. J Mater Proces Technol 129(1–3):200–206

    Article  Google Scholar 

  13. Thangavel P, Selladurai V, Shanmugam R (2006) Application of response surface methodology for predicting flank wear in turning operation. J Eng Manuf 220(6):997–1003

    Article  Google Scholar 

  14. Dolinšek S, Šuštaršic B, Kopac J (2001) Wear mechanisms of cutting tools in high-speed cutting processes. Wear 250(1–12):349–356

    Article  Google Scholar 

  15. Dodier RH, Henze GP (2004) Statistical analysis of neural networks as applied to building energy prediction. J Sol Energy Eng 126(1):592–600

    Article  Google Scholar 

  16. Ibnkahla M (2001) Convergence properties and stationary points of the two-layer backpropagation algorithm used for non-lineal function modeling. In: Proceedings of the international joint conference on neural networks, Washington, DC, USA

  17. Ezugwu EO, Arthur SJ, Hines EL (1995) Tool-wear prediction using artificial neural networks. J Mater Proces Technol 49(3–4):255–264

    Article  Google Scholar 

  18. Li X, Nee AYC (1996) Monitoring cutting conditions for tool scheduling in CNC machining. Manuf Syst 25(4):377–383

    Google Scholar 

  19. Chao PY, Hwang YD (1997) An improved neural network model for the prediction of cutting tool life. J Intell Manuf 8(2):107–115

    Article  Google Scholar 

  20. Niranjan Prasad K, Ramamoorthy B (2001) Tool wear evaluation by stereo vision and prediction by artificial neural network. J Mater Process Technol 112(1):43–52

    Article  Google Scholar 

  21. Lin JT, Bhattacharyya D, Kecman V (2003) Multiple regression and neural networks analyses in composites machining. Compos Sci Technol 63(3–4):539–548

    Article  Google Scholar 

  22. Özel T, Karpat Y (2005) Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks. Int J Mach Tools Manuf 45(4–5):467–479

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Mechanical Engineering, University of Matanzas, Autopista a Varadero km 31/2, Matanzas, 44740, Cuba

    Ramón Quiza

  2. Department of Mechanical Engineering, University of Aveiro Campus Santiago, 3810-193, Aveiro, Portugal

    Luis Figueira & J. Paulo Davim

Authors
  1. Ramón Quiza
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  2. Luis Figueira
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  3. J. Paulo Davim
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Correspondence to J. Paulo Davim.

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Quiza, R., Figueira, L. & Paulo Davim, J. Comparing statistical models and artificial neural networks on predicting the tool wear in hard machining D2 AISI steel. Int J Adv Manuf Technol 37, 641–648 (2008). https://doi.org/10.1007/s00170-007-0999-7

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  • DOI: https://doi.org/10.1007/s00170-007-0999-7

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