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Prediction of depth of cut for single-pass laser micro-milling process using semi-analytical, ANN and GP approaches

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Abstract

The present study is aimed to investigate micro-milling performance of thermoplastics with different parameters, namely laser beam absorptivity, latent heat of vaporization, laser power and cutting speed. The 25-W CO2 (CW) laser engraving machine is used for the investigation. In total 50 different combinations of laser power and cutting speed with four categories of thermoplastics, namely poly-methyl-methacrylate, poly-propylene, acrylonitrile butadiene styrene and nylon 6, are used in this study. Experimental results suggest that laser beam absorptivity, cutting power and cutting speed are the major influencing parameters on depth of cut. Theoretical model for the prediction of depth of cut in terms of material properties, cutting power and cutting speed has been proposed. Two correction parameters have been introduced in this analysis using non-linear regression method to improve the theoretical model. Comparison has been made between prediction capabilities of theoretical model, model based on multi-gene genetic programming and artificial neural network. The comparison clearly indicates that all the three models provide accurate prediction of depth of cut. The details of experimentation, model development, testing and the performance comparison are presented in this paper.

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References

  1. Dahotre N, Harimkar S (2008) Laser fabrication and machining of materials. Springer, USA, pp 34–37

    Google Scholar 

  2. Davim JP, Oliveira C, Barricas N, Conceicao M (2007) Some experimental studies on CO2 laser cutting quality of polymeric materials. J Mater Process Technol 198:99–104

    Article  Google Scholar 

  3. Mustafa K, Kaynak Y, Bagci E, Demirer H, Kurt M (2008) Dimensional analyses and surface quality of the laser cutting process for engineering plastics. Int J Adv Manuf Technol 41:259–267

    Google Scholar 

  4. Ready JF (2001) LIA handbook of laser materials processing. Laser Institute of America, USA, pp 530–533

    Google Scholar 

  5. Norikazu T, Shigenori Y, Masao H (1996) Present and future of lasers for fine cutting of metal plate. J Mater Process Technol 62:309–314

    Article  Google Scholar 

  6. Yilbas BS, Decies R, Yilbas Z (1992) Study into penetration speed during CO2 laser cutting of stainless steel. Opt Lasers Eng 17:69–82

    Article  Google Scholar 

  7. Thawari G, Sarin Sundar JK, Sundararajan G, Joshi SV (2005) Influence of process parameters during pulsed Nd:YAG laser cutting of nickel-base super alloys. J Mater Process Technol 170:229–239

    Article  Google Scholar 

  8. Chen SL (1999) The effects of high-pressure assistant-gas flow on high-power CO2 laser cutting. J Mater Process Technol 88:57–66

    Article  Google Scholar 

  9. Lu G, Siores E, Wang B (1999) An empirical equation for crack formation in the laser cutting of ceramic plates. J Mater Process Technol 88:154–158

    Article  Google Scholar 

  10. Zhou BH, Mahdavian SM (2004) Experimental and theoretical analyses of cutting non metallic materials by low power CO2-laser. J Mater Process Technol 146:188–192

    Article  Google Scholar 

  11. Sheng P, Chryssolouris G (1995) Theoretical model of laser grooving for composite materials. J Compos Mater 29:96–112

    Article  Google Scholar 

  12. Cai L, Sheng P (1996) Analysis of laser evaporative and fusion cutting. ASME J Manuf Sci Eng 118:225–234

    Article  Google Scholar 

  13. Li Y, Latham WP, Kar A (2001) Lumped parameter model for multimode laser cutting. Opt Lasers Eng 35:371–386

    Article  Google Scholar 

  14. Majumdar P, Chen ZH, Kim MJ (1994) Evaporative material removal process with a continuous wave laser. Comp Struct 57:663–671

    Article  Google Scholar 

  15. Meung JK, Zhang J (1999) Finite element analysis of evaporative cutting with a moving high energy pulsed laser. Appl Math Model 25:203–220

    Google Scholar 

  16. Meung JK (2004) Transient evaporative laser cutting with moving laser by boundary element method. Appl Math Model 28:891–990

    Article  MATH  Google Scholar 

  17. Masmitari N, Philip PK (2007) Investigations on laser percussion drilling of some thermoplastic polymers. J mater process technol 185:198–203

    Article  Google Scholar 

  18. Caiazzo F, Curcio F, Daurelio G, Minutolo FMC (2005) Laser cutting of different polymeric plastics (PE, PP and PC) by a CO2 laser beam. J Mater Process Technol 159:279–285

    Article  Google Scholar 

  19. Dhupal D, Doloi B, Bhattacharyya B (2008) Parametric analysis and optimization of Nd:YAG laser micro-grooving of aluminium titanate (Al2TiO5) ceramics. Int J Adv Manuf Technol 36:883–893

    Article  Google Scholar 

  20. Kibria G, Doloi B, Bhattacharyya B (2010) Experimental analysis on Nd:YAG laser micro-turning of alumina ceramic. Int J Adv Manuf Technol 50:643–650

    Article  Google Scholar 

  21. Orazi L, Cuccolini G, Fortunato A, Tani G (2010) An automated procedure for material removal rate prediction in laser surface micro manufacturing. Int J Adv Manuf Technol 46:163–171

    Article  Google Scholar 

  22. Saklakoglu IE, Kasman S (2011) Investigation of micro-milling process parameters for surface roughness and milling depth. Int J Adv Manuf Technol 54:567–578

    Article  Google Scholar 

  23. Snakenborg D, Klank H, Kutter JP (2004) Microstructure fabrication with a CO2 laser system. J Micromech Microeng 14:182–189

    Article  Google Scholar 

  24. Black SLN, Lum KCP (2000) CO2 laser cutting of MDF: estimation of power distribution. Opt Laser Technol 32:77–87

    Article  Google Scholar 

  25. GCC LaserPro product (2006) Laser Pro-II, technical manual

  26. Caprino G, Tagliaferri V, Covelli L (1995) Cutting glass fibre reinforced composites using CO2 laser with multimodal-Gaussian distribution. Int J Mach Tools Manuf 35:831–840

    Article  Google Scholar 

  27. Choudhury IA, Shirley S (2010) Laser cutting of polymeric materials: an experimental investigation. Opt Laser Technol 42:503–508

    Article  Google Scholar 

  28. Atanasov PA, Baeva MG (1993) CW CO2 laser cutting of plastics. SPIE Proc conf Appl Lasers ind 3092:772–775

    Google Scholar 

  29. Black I (1998) Laser cutting speeds for ceramic tile: a theoretical and-empirical comparison. Int J Adv Manuf Technol 30:95–101

    Google Scholar 

  30. Bates DM, Watts DG (2008) Nonlinear regression analysis and its applications. Wiley, USA, pp 71–86

    Google Scholar 

  31. Marquardt D (1963) A algorithm for least squares estimation of nonlinear parameters. J Appl Math 11:431–441

    MathSciNet  MATH  Google Scholar 

  32. Levenberg K (1944) A method for the solution of certain problems in least squares. Quart Appl Math 2:164–168

    MathSciNet  MATH  Google Scholar 

  33. Brezocnik M, Balic J, Kampus Z (2001) Modelling of forming efficiency using genetic programming. J Mater Process Technol 109:20–29

    Article  Google Scholar 

  34. Brezocnik M, Kovacic M, Ficko M (2004) Prediction of surface roughness with genetic programming. J Mater Process Technol 157:28–36

    Article  Google Scholar 

  35. Nastran M, Balic J (2002) Prediction of metal wire behaviour using genetic programming. J Mater Process Technol 122:368–373

    Article  Google Scholar 

  36. Kojima F, Kubota N, Hashimoto S (2001) Identification of crack profiles using genetic programming and fuzzy inference. J Mater Process Technol 108:263–267

    Article  Google Scholar 

  37. Cevik A (2008) Unified formulation for ultimate capacity of shear failure of arc spot welding using genetic programming. J Mater Process Technol 204:117–124

    Article  Google Scholar 

  38. Searson DP (2009) GPTIPS: Genetic Programming & Symbolic Regression for MATLAB. http://gptips.sourceforge.net

  39. Hinchliffe MP, Willis MJ, Hiden H, Tham MT, McKay B, Barton GW (1996) Modeling chemical process systems using a multi-gene genetic programming algorithm. In: Genetic programming: Proceedings of the first annual conference (late breaking papers). MIT, USA, pp 56–65

  40. Searson DP, Leahy DE, Willis MJ (2011) Predicting the toxicity of chemical compounds using GPTIPS: a free genetic programming toolbox for MATLAB. Int Control Comp Eng Lect Notes Electr Eng 70:83–93

    Google Scholar 

  41. MATLAB MathWorks Inc. (2010) MATLAB user manual version 7.11. R2010b

  42. Haykin S (1999) Neural networks—a comprehensive foundation. Prentice-Hall, Upper Saddle River

    MATH  Google Scholar 

  43. Kasabov NK (1996) Foundations of neural networks, fuzzy systems, and knowledge engineering. MIT, Cambridge

    MATH  Google Scholar 

  44. Caglar N, Pala M, Elmas M, Erylmaz DM (2009) A new approach to determine the base shear of steel frame structures. J Constr Steel Res 65:188–195

    Article  Google Scholar 

  45. 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 25:118–129

    Article  Google Scholar 

  46. Freeman J, Skapura D (1990) Neural networks algorithms: application and programming techniques. Addison Wisely, New York

    Google Scholar 

  47. Inamdar MV, Date PP, Desai UB (2000) Studies of the prediction of springback in air vee bending of metallic sheets using an artificial neural network. J Mater Process Technol 108:45–54

    Article  Google Scholar 

  48. Hornik KN, Stinchcombe M, White H (1990) Multilayer feed foreword networks are universal approximators. Neural Networks 2:359–366

    Article  Google Scholar 

  49. Maren AJ, Jones D, Franklin S (1990) Configuring and optimizing the back propagation network. Handbook of neural computing. Academic, New York

    Google Scholar 

  50. Xie X, Li L, Wei X, Hu W (2008) Distribution of the intensity absorbed by evaporative front in laser cutting non-metallic material. Opt Lasers Eng 46:604–613

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Mechanical Engineering, C.G. Patel Institute of Technology, Bardoli, India

    Chinmay K. Desai

  2. Department of Mechanical Engineering, S.V. National Institute of Technology, Surat, India

    Abdulhafiz Shaikh

Authors
  1. Chinmay K. Desai
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  2. Abdulhafiz Shaikh
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Correspondence to Chinmay K. Desai.

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Desai, C.K., Shaikh, A. Prediction of depth of cut for single-pass laser micro-milling process using semi-analytical, ANN and GP approaches. Int J Adv Manuf Technol 60, 865–882 (2012). https://doi.org/10.1007/s00170-011-3677-8

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  • DOI: https://doi.org/10.1007/s00170-011-3677-8

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