Skip to main content

Advertisement

Springer Nature Link
Log in

Process parameters optimization for micro end-milling operation for CAPP applications

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

In Computer Aided Process Planning (CAPP), process parameters selection for the given manufacturing feature is the final activity and it is the key area for research and development. In this work, an attempt has been made to optimize parameters for micro end-milling operation as a part of CAPP system development for micromachining processes using Artificial Intelligence (AI) approach. Genetic Algorithm (GA) has been found to be the robust and efficient tool to solve nonlinear optimization problems involved in process planning. Microfeatures of size 0.7 and 1 mm are considered and polymethyl methacrylate is chosen as the work material due to its potential application in microparts fabrication. Initially, experimental investigation has been carried out to analyze the impact of process conditions such as spindle speed and feed rate on surface roughness and machining time. Further multiobjective optimization for minimization of responses is carried out using GA. Finally, confirmation experiments are carried out to validate the accuracy of GA results. The optimized process parameters are stored in the database and it ensures foolproof parameters for micro end-milling operation for CAPP applications apart from manuals and catalogues. The proposed approach can be repeated for various other end mill features and for different work and tool material combination to ensure a complete parameters selection module for CAPP system applications.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Asad ABMA, Masaki T, Rahman M, Lim HS, Wong YS (2007) Tool-based micromachining. J Mater Proc Technol 192–193:204–211

    Article  Google Scholar 

  2. Radhakrishnan P, Subramanyan S, Raju V (2000) Automated process planning. CAD/CAM/CIM, 2nd edn. New Age International (P) Ltd, New Delhi, pp 223–259

    Google Scholar 

  3. Waiyagan K, Bohez EL (2009) Intelligent feature based process planning for five-axis mill-turn parts. Comput Ind 60:296–316

    Article  Google Scholar 

  4. Deb S, Parra-Castillo JR, Ghosh K (2011) An integrated and intelligent computer-aided process planning methodology for machined rotationally symmetrical parts. Int J Adv Manuf Syst 13:1

    Google Scholar 

  5. Champati S, Lu WF, Lin AC (1996) An automated operation sequencing in intelligent process planning: a case based approach. Int J Adv Manuf Technol 12:21–36

    Article  Google Scholar 

  6. Rahimic S, Visekruna V (2007) Optimization of generative process planning system with minimum cost per piece. Adv Prod Eng Manag 2:177–184

    Google Scholar 

  7. Alam MR, Lee KS, Rahman M, Zhang YF (2000) Automated process planning for the manufacture of sliders. Comput Ind 43:249–262

    Article  Google Scholar 

  8. Jiang B, Lau H, Chan FTS, Jiang H (1999) An automatic process planning system for the quick generation of manufacturing process plans directly from CAD drawings. J Mater Proc Technol 87:97–106

    Article  Google Scholar 

  9. Younis MA, Abdel Wahab MA (1997) A CAPP expert system for rotational components. Comput Ind Eng 33(3–4):509–512

    Article  Google Scholar 

  10. Deb S, Ghosh K, Paul S (2006) A neural network based methodology for machining operations selection in computer-aided process planning for rotationally symmetrical parts. J Intel Manuf 17:557–569

    Article  Google Scholar 

  11. Salehi M, Tavakkoli-Moghaddam R (2009) Application of genetic algorithm to computer-aided process planning in preliminary and detailed planning. Eng Appl Artif Intel 22:1179–1187

    Article  Google Scholar 

  12. Dereli T, Huseyin Filiz I (1999) Optimization of process planning functions by genetic algorithms. Comput Ind Eng 36:281–308

    Article  Google Scholar 

  13. Khoshnevis B, Tan W (1995) Automated process planning for hole-making. Am Soc Mech Eng Manuf Rev 8(2):106–113

    Google Scholar 

  14. Devireddy CR, Eid T, Ghosh K (2002) Computer-aided process planning for rotational components using artificial neural networks. Int J Agile Manuf 5(1):27–49

    Google Scholar 

  15. Wong TN, Siu SL (1995) A knowledge-based approach to automated machining process selection and sequencing. Int J Prod Res 33(12):3465–3484

    Article  MATH  Google Scholar 

  16. Shunmugam MS, Mahesh P, Bhaskara Reddy SV (2002) A method of preliminary planning for rotational components with C-axis features using genetic algorithm. Comput Ind 48(3):199–217

    Article  Google Scholar 

  17. Edalew KO, Abdalla HS, Nash RJ (2001) A computer-based intelligent system for automatic tool selection. Mater Design 22(5):337–351

    Article  Google Scholar 

  18. Sivasankar R, Asokan P, Prabhakaran G, Phani AV (2008) A CAPP framework with optimized process parameters for rotational components. Int J Prod Res 46(20):5561–5587

    Article  Google Scholar 

  19. Rahimic S, Visekruna V (2007) Optimization of generative process planning system with minimum cost per piece. Adv Prod Eng Manag 2:177–184

    Google Scholar 

  20. Kayacan MC, Filiz IH, Sonmey AI, Baykasoglu A, Dereli T (1996) OPPS-ROT: an optimized process planning system for rotational parts. Comput Ind 32:181–195

    Article  Google Scholar 

  21. Prasad AVSRK, Rao PN, Rao URK (1997) Optimal selection of process parameters for turning operation in a CAPP system. Int J Prod Res 35:1495–1522

    Article  MATH  Google Scholar 

  22. Pande SS, Palsule NH (1988) GCAPPS—a computer assisted generative process planning system for turned components. Comput Aided Eng J 5:163–168

    Article  Google Scholar 

  23. Pande SS, Walvekar MG (1989) PC-CAPP—a computer assisted process planning system for prismatic components. Comput Aided Eng J 6:133–138

    Article  Google Scholar 

  24. Saravanan R, Sankar RS, Asokan P, Vijayakumar K, Prabhaharan G (2005) Optimization of cutting conditions during continuous finished profile machining using non-traditional techniques. Int J Adv Manuf Technol 26:30–40

    Article  Google Scholar 

  25. Baskar N, Asokan P, Saravanan R, Prabhaharan G (2005) Optimization of machining parameters for milling operations using non-conventional methods. Int J Adv Manuf Technol 25:1078–1088

    Article  Google Scholar 

  26. Xu H, Li D (2009) Modeling of process parameter selection with mathematical logic for process planning. Robot Comput Integr Manuf 25:529–535

    Article  Google Scholar 

  27. Wang W, Kweon SH, Yang SH (2005) A study on roughness of the micro-end-milled surface produced by a miniatured machine tool. J Mater Proc Technol 162–163:702–708

    Article  Google Scholar 

  28. Ali MY, Omar MA, Othman KI (2009) Prediction of burr formation in fabricating MEMS components by micro end milling. J Adv Mater Res 74:247–250

    Article  Google Scholar 

  29. Jaeblon T (2010) Polymethylmethacrylate: properties and contemporary uses in orthopedics. J Am Acad Orthop 18(5):297–305

    Google Scholar 

  30. http://en.wikipedia.org/wiki/Poly(methyl_methacrylate)

  31. Zain AM, Haron H, Sharif S (2010) Application of GA to optimize cutting conditions for minimizing surface roughness in end milling machining process. J Expert Syst Appl 37:4650–4659

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Production Engineering, National Institute of Technology, Tiruchchirappalli, 620015, India

    S. P. Leo Kumar, J. Jerald, S. Kumanan & Nargundkar Aniket

Authors
  1. S. P. Leo Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. J. Jerald
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. S. Kumanan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Nargundkar Aniket
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S. P. Leo Kumar.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Leo Kumar, S.P., Jerald, J., Kumanan, S. et al. Process parameters optimization for micro end-milling operation for CAPP applications. Neural Comput & Applic 25, 1941–1950 (2014). https://doi.org/10.1007/s00521-014-1683-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-014-1683-0

Keywords