Advertisement

Use of NC kernel data for surface roughness monitoring in milling operations

  • Christian Brecher
  • Guillem Quintana
  • Thomas Rudolf
  • Joaquim Ciurana
ORIGINAL ARTICLE

Abstract

This work focuses on developing an application based on the information contained in the numerical control (NC) kernel for surface roughness monitoring of the part in process. A human–machine interface (HMI) was developed in order to facilitate the interaction between the operator and the NC kernel with a graphical user interface working in the computer numerically controlled (CNC) screen. Experimentation was carried out in order to obtain the data to be modeled with artificial neural networks for surface roughness average parameter (Ra) predictions. Finally, a compact solution was implemented through global user data (GUD). Data from the HMI and from the kernel are collected in the GUD and analyzed with the artificial neural network. The application provides the surface roughness average parameter of the part in process and gives optimized parameters to the operator. Verification tests were carried out, showing accurate results. The use of the application developed in this research ensures the surface roughness Ra requirement, improves cutting parameters, reduces manual finishing operations and unacceptable parts at the end of the manufacturing process, and provides a solution implemented in the machine tool CNC screen without the need of any other external sensors.

Keywords

Milling Surface roughness Kernel Process monitoring Cutting parameters Human machine interface 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    López de Lacalle LN, Lamikiz A (eds) (2008) Machine tools for high performance machining. Springer, HeidelbergGoogle Scholar
  2. 2.
    Benardos PG, Vosniakos G (2003) Predicting surface roughness in machining: a review. Int J Mach Tools Manuf 43(8):833–844CrossRefGoogle Scholar
  3. 3.
    Groover MP, Society of Manufacturing Engineers (2004) Fundamentals of modern manufacturing: materials, processes, and systems, 2nd edn. Wiley, New YorkGoogle Scholar
  4. 4.
    Correa M, Bielza C, Pamies-Teixeira J (2009) Comparison of Bayesian networks and artificial neural networks for quality detection in a machining process. Expert Syst Appl 36:7270–7279CrossRefGoogle Scholar
  5. 5.
    Schulz H (1995) High-speed milling of dies and moulds—cutting conditions and technology. CIRP Ann Manuf Technol 44(1):35–38MathSciNetCrossRefGoogle Scholar
  6. 6.
    Weck M, Plapper V (2001) Sensorless machine tool condition monitoring based on open NCs. In: IEEE Power Engineering Society, International Conference on Robotics and Automation, IEEE Operations Center ERGoogle Scholar
  7. 7.
    Brecher C, Rudolf T (2010) In process identification of cutting condition using digital drive signals. In: Altintas Y, Denkena B, Brecher C (eds) Proceedings of the CIRP 2nd International Conference Process Machine Interactions Vancouver, BC, CanadaGoogle Scholar
  8. 8.
    Pritschow G, Altintas Y, Jovane F, Koren Y, Mitsuishi M, Takata S et al (2001) Open controller architecture—past, present and future. CIRP Ann Manuf Technol 50(2):463–470CrossRefGoogle Scholar
  9. 9.
    Kaever M, Brouer N, Rehse M, Weck M (1997) NC integrated process monitoring and control for intelligent, autonomous manufacturing systems. In: New Manufacturing Era, Osaka, pp 69–74Google Scholar
  10. 10.
    Martellotti ME (1941) An analysis of the milling process. Trans ASME 63:667Google Scholar
  11. 11.
    Arizmendi M, Fernández J, Gil A, Veiga F (2009) Effect of tool setting error on the topography of surfaces machined by peripheral milling. Int J Mach Tools Manuf 49(1):36–52CrossRefGoogle Scholar
  12. 12.
    Quintana G, Ribatallada J, Ciurana Q (2010) Surface roughness generation and material removal rate in ball end milling operations. Mater Manuf Process 25:386–398CrossRefGoogle Scholar
  13. 13.
    Antoniadis A, Savakis C, Bilalis N, Balouktsis A (2003) Prediction of surface topomorphy and roughness in ball-end milling. Int J Adv Manuf Technol 21(12):965–971CrossRefGoogle Scholar
  14. 14.
    Beggan C, Woulfe M, Young P, Byrne G (1999) Using acoustic emission to predict surface quality. Int J Adv Manuf Technol 15(10):737–742CrossRefGoogle Scholar
  15. 15.
    Huang B, Chen JC (2003) An in-process neural network-based surface roughness prediction (INN-SRP) system using a dynamometer in end milling operations. Int J Adv Manuf Technol 21(5):339–347zbMATHCrossRefGoogle Scholar
  16. 16.
    Zhang JZ, Chen JC (2007) The development of an in-process surface roughness adaptive control system in end milling operations. Int J Adv Manuf Technol 31(9–10):877–887CrossRefGoogle Scholar
  17. 17.
    Alauddin M, El Baradie MA, Hashmi MSJ (1995) Computer-aided analysis of a surface-roughness model for end milling. J Mater Process Technol 55(2):123–127CrossRefGoogle Scholar
  18. 18.
    Alauddin M, El Baradie MA, Hashmi MSJ (1996) Optimization of surface finish in end milling Inconel 718. J Mater Process Technol 56(1–4):54–65CrossRefGoogle Scholar
  19. 19.
    Yang WH, Tarng YS (1998) Design optimization of cutting parameters for turning operations based on the Taguchi method. J Mater Process Technol 84(1–3):122–129CrossRefGoogle Scholar
  20. 20.
    Choudhury SK, Bartarya G (2003) Role of temperature and surface finish in predicting tool wear using neural network and design of experiments. Int J Mach Tools Manuf 43(7):747–753, /5CrossRefGoogle Scholar
  21. 21.
    Thangavel P, Selladurai V (2008) An experimental investigation on the effect of turning parameters on surface roughness. Int J Manuf Res 3(3):285–300CrossRefGoogle Scholar
  22. 22.
    Dhokia VG, Kumar S, Vichare P, Newman ST (2008) An intelligent approach for the prediction of surface roughness in ball-end machining of polypropylene. Rob Comput Integr Manuf 24(6):835–842CrossRefGoogle Scholar
  23. 23.
    Lou MS, Chen JC, Li CM (1998) Surface roughness prediction technique for CNC end-milling. J Ind Tech 15(1):1–6Google Scholar
  24. 24.
    Benardos PG, Vosniakos GC (2002) Prediction of surface roughness in CNC face milling using neural networks and Taguchi’s design of experiments. Rob Comput Integr Manuf 18(5–6):343–354CrossRefGoogle Scholar
  25. 25.
    Ozel 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):467–479CrossRefGoogle Scholar
  26. 26.
    Nebot JVA, Morales-Menéndez R, Vallejo AJ, Rodríguez CA, Subiron FR (2006) Comparison of modelling approaches in surface roughness and cutting tool-wear condition for face milling operations. In: CIRP, 2nd International Conference on High Performance Cutting (HPC)Google Scholar
  27. 27.
    Nebot JVA, Morales-Menéndez R, Vallejo Guevarra AJ, Rodríguez CA, Subiron FR (2006) Surface roughness and cutting tool-wear diagnosis based on Bayesian networks. In: 6th IFAC Symposium on Fault Detection, Supervision and Safety of Technical ProcessesGoogle Scholar
  28. 28.
    Shie J (2006) Optimization of dry machining parameters for high-purity graphite in end-milling process by artificial neural networks: a case study. Mater Manuf Processes 21(8):838–845CrossRefGoogle Scholar
  29. 29.
    Correa M, Bielza C, Ramirez MDJ, Alique JR (2008) A Bayesian network model for surface roughness prediction in the machining process. Int J Syst Sci 39(12):1181–1192zbMATHCrossRefGoogle Scholar
  30. 30.
    Samanta B, Nataraj C (2008) Surface roughness prediction in machining using computational intelligence. Int J Manuf Res 3(4):379–392CrossRefGoogle Scholar
  31. 31.
    Samanta B, Erevelles W, Omurtag Y (2008) Prediction of workpiece surface roughness using soft computing. Proc Inst Mech Eng B J Eng Manuf 222(10):1221–1232CrossRefGoogle Scholar
  32. 32.
    Samanta B (2009) Surface roughness prediction in machining using soft computing. Int J Comput Integr Manuf 22(3):257–266CrossRefGoogle Scholar
  33. 33.
    Quintana G, Garcia-Romeu ML, Ciurana J (2010) Surface roughness monitoring application based on artificial neural networks for ball-end milling operations. J Intell Manuf (in press)Google Scholar
  34. 34.
    Brecher C, Lohse W, Vitr M (2010) CAx framework for planning five-axis milling processes. In: Huang GQ, Mak KL (eds) In: Proceedings of the 6th CIRP-sponsored International Conference on Digital Enterprise Technology Berlin KW, pp 419–432Google Scholar
  35. 35.
    SinuCom (2004) Stand-alone trace server. SinuCom NC version 6.3Google Scholar

Copyright information

© Springer-Verlag London Limited 2010

Authors and Affiliations

  • Christian Brecher
    • 1
  • Guillem Quintana
    • 2
  • Thomas Rudolf
    • 1
  • Joaquim Ciurana
    • 3
  1. 1.Laboratory for Machine Tools and Production EngineeringRWTH Aachen University of TechnologyAachenGermany
  2. 2.ASCAMM Technology CentreCerdanyola del Vallès, BarcelonaSpain
  3. 3.Department of Mechanical engineering and civil constructionUniversitat de GironaGironaSpain

Personalised recommendations