Skip to main content
SpringerLink
Log in

Using flower pollinating with artificial bees (FPAB) technique to determine machinable volumes in process planning for prismatic parts

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Process planning (PP) has an important role in manufacturing systems design and operations. Volume decomposition and machinable volumes (MVs) or machining features determination is the core activity in process planning. This process requires extraction of elementary volumes (EVs), merging or clustering EVs to construct feasible MVs and finally selecting an optimal combination of MVs. Development of MVs is an important activity, so that better solution is obtained by better developed MVs. Generation of limited number of MVs or machining features, which is often performed by experts may miss the optimal solution. Also, using exact methods such as combinatorial optimization not only generate infeasible MVs, but also require an excessive amount of computational time. In this research, the meta-heuristic procedure of flower pollinating by artificial bees (FPAB) is used in manufacturing context to generate and assess the feasibility of MVs. Furthermore, a set-covering method is used to select the optimal solution. The performance of the proposed model is assessed through some numerical examples. The encouraging results of the numerical examples demonstrate good performance of the proposed method in machining feature or machinable volumes determination problem (MVDP).

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Abouel Nasr ES, Kamrani AK (2006) A new methodology for extracting manufacturing features from CAD systems. Comput Ind Eng 51:389–415. doi:10.1016/j.cie.2006.08.004

    Article  Google Scholar 

  2. Babic B, Nesic N, Miljkouic Z (2008) A review of automated feature recognition with rule based pattern recognition. Comput Ind 59:321–337. doi:10.1016/j.compind.2007.09.001

    Article  Google Scholar 

  3. Bhaskara Reddy SV (1999) Operation sequencing in CAPP using genetic algorithms. Int J Prod Res 37:1063–1074

    Article  MATH  Google Scholar 

  4. Bonabeau E, Dorigo M, Theraulaz G (1999) Swarm Intelligence: from natural to artificial systems. Oxford University Press, NY

    MATH  Google Scholar 

  5. Chang PC (1995) A practical approach for parts scheduling with alternative process plans in automated manufacturing systems. Comput Math Appl 28(9):55–65

    Article  Google Scholar 

  6. Ciurana J, Garcia-Romeu ML, Castero R, Alberti M (2003) A system based on machined volumes to reduce the number of route sheets in process planning. Computers Ind 51(1):41–50

    Article  Google Scholar 

  7. Deneubourg JL, Goss S, Francs N, Sendova-Franks A, Detrain C, Chretien L (1991) The dynamics of collective sorting: robot-like ant and ant-like robot. In: Meyer JA, Wilson SW (eds) In proceedings of the first conference on simulation of adaptive behavior: from animals to animates. MIT Press, Cambridge, MA, pp 356–365

    Google Scholar 

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

    Article  Google Scholar 

  9. Devireddy CR (1999) Feature-based modelling and neural networks-based CAPP for integrated manufacturing. Int J Comput Integrated Manuf 12:61–74

    Article  Google Scholar 

  10. Ding L, Yue Y, Ahmet K, Jackson M, Parkin R (2005) Global optimization of a feature-based process sequence using GA and ANN techniques. Int J Prod Res 43(15):3247–3272

    Article  MATH  Google Scholar 

  11. Eberhart, R. and Kennedy, J., A New Optimizer Using Particles Swarm Theory, Proceeding Sixth International Symposium on Micro Machine and Human Science, PP. 39-43, 1995.

  12. Eberhart R, Shi Y (1998) Comparison between genetic algorithms and particle swarm optimization, The 7th Annual Conference on Evolutionary Programming, San Diego, USA

  13. Gao, L., Gao, H., Zhou, C., Particle swarm optimization based algorithm for machining parameter optimization, Proceeding of the 5th World Congress on Intelligent Control and Automation, June 15-19, 2004.

  14. Geelink R, Salomons OW, Van Slooten F, Houten FJAMV (1995) Unified feature definition for feature-based design and feature-based modeling. In A.A. Busnaina, editor, ASME Computers in Engineering Conference, pp 517–534, New York, NY 10017, September 17–20, Boston, MA 1995. ASME

  15. Granville C (1995) Computer-aided process planning. Comput Aided Eng 8:46–48

    Google Scholar 

  16. Guo YW, Li WD, Mileham AR, Owen GW (2009) Applications of particle swarm optimisation in integrated process planning and scheduling. Robot Comput-Integr Manuf 25:280-288

    Article  Google Scholar 

  17. Gupta SK (1994) Automated manufacturability analysis of machined parts. PhD Thesis, The University of Maryland, USA

  18. Gupta SK, Nau DS (1995) Systematic approach to analyzing the manufacturability of machined parts. Comput Aided Des 27(5):323–342

    Article  MATH  Google Scholar 

  19. Han JH (1996) 3D geometric reasoning algorithms for feature recognition. PhD Thesis, The University of Southern California, USA

  20. Han JH, Pratt M, Regli WC (2000) Manufacturing feature recognition from solid models: a status report. IEEE Trans Robot Autom 16:782–789

    Article  Google Scholar 

  21. Handl J, Meyer B (2002) Improved ant-based clustering and sorting in a document retrieval interface.” In proceedings of the 7th international conference on parallel problem solving from nature. 913–923

  22. Houshmand M, Imani DM, Akbari M A (2007) heuristic model to generate, assess and select feasible machining features of prismatic parts”, Proceeding 4th international conference on Digital Enterprise Technology, Bath, United Kingdom 734–741, 19-21, September

  23. Houten, FJAMV (1991) PART: A comptuter aided process planning system. PhD thesis, University of Twente

  24. Ji Q, Marefat MM, Lever PJ (1995) Evidential reasoning approach for recognizing shape features. In proceeding of the 11th conference on artificial intelligence for applications. pp 162-168

  25. Joshi S, Chang TC (1988) Graph based heuristics for recognition of machined features from a 3-D solid model. Comput-Aided Des 20:58–66

    Article  MATH  Google Scholar 

  26. Kazemian M, Ramezani Y, Lucas C, Moshiri B (2006) Swarm clustering based on flowers pollination by artificial bees. Swarm Intelligence in Data Mining 34:191–202

    Article  Google Scholar 

  27. Kennedy J, Spears W (1998) Matching algorithms to problems: An experimental test of the particle swarm and some genetic algorithms on the multimodal problem generator, IEEE International Conference on Evolutionary Computation, Anchorage, Alaska, USA

  28. Khan Z, Prasad B, Singh T (1997) Machining condition optimization by genetic algorithms and simulated annealing. Comput Oper Res 24(7):647–659

    Article  MATH  Google Scholar 

  29. Khoshnevis B, Sormaz DN, Park JY (1999) An integrated process planning system using feature reasoning and space search-based optimization. IIE Transactions 31:597–616

    Google Scholar 

  30. Kim YS, Wang E (2002) Recognition of machining features for cast then machined parts. Comput Aided Des 34:71–87

    Article  Google Scholar 

  31. Koroseca M, Balic J, Kopaca J (2004) Neural network based manufacturability evaluation of free form machining. Int J Mach Tool Manufact 45(1):13–20

    Article  Google Scholar 

  32. Kryssanov VV, Kleshchev AS, Fukuda Y, Konishi K (1998) Building a logical model in the machining domain for CAPP experts systems. Int J Prod Res 36:939–956

    Article  Google Scholar 

  33. Kueng KW, Yuen D (2003) An intelligent hierarchical work station control model. J Mater Process Tech 139:134–139

    Article  Google Scholar 

  34. Kusiak A (1985) Integer programming approach to process planning. Int J Adv Manuf Tech 1:73–83

    Article  Google Scholar 

  35. Kusiak A (2000) Computational intelligence in design and manufacturing. Wiley, USA

    Google Scholar 

  36. Labroche N, Monmarch N, Venturini G (2002) A new clustering algorithm based on the chemical recognition system of ants. In proceedings of the European conference on artificial intelligence, Lyon, France, 345–349

  37. Lee JY, Kim K (1999) Generating alternative interpretations of machining features. Int J Adv Manuf Tech 15:38–48

    Article  Google Scholar 

  38. Li WD, McMahon CA (2007) A simulated annealing-based optimization approach for integrated process planning and scheduling. Int J Comput Integr Manuf 20(1):80–95

    Article  Google Scholar 

  39. Li WD, Ong SK, Nee AYC (2002) Hybrid genetic algorithm and simulated annealing approach for the optimization of process plans for prismatic parts. Int J Prod Res 40(8):1899–1922

    Article  MATH  Google Scholar 

  40. Lin AC, Lin SY, Diganta D, Lu WF (1998) An integrated approach to determining the sequence of machining operations for prismatic parts with interacting features. J Mater Process Tech 73:234–250

    Article  Google Scholar 

  41. Lu H, Maranzana R, Mascle C (2007) Decomposition of delta volume for machining. 12th IFToMM World Congress, Besancon

  42. Lumer E, Faieta B (1994) Diversity and adaptation in populations of clustering ants. In proceedings of the third international conference on simulation of adaptive behavior: from animals to animates 3. MIT Press, Cambridge, MA, pp 499–508

    Google Scholar 

  43. Monmarch N, Slimane M, Venturini G (1999) On improving clustering in numerical databases with artificial ants: Advances in artificial life. 5th European conference, ECAL’99, Proceedings of lecture notes in artificial intelligence 1674:626–635

  44. Morad N, Zalzala A (1999) Genetic algorithms in integrated process planning and scheduling. J Intell Manuf 10(2):169–179

    Article  Google Scholar 

  45. Nagaraj HS, Gurumoorthy B (2002) Machinable volume extraction for automatic process planning. IIE Transactions 34:393–410

    Google Scholar 

  46. Norhashimah M, Ams Z (1999) Genetic algorithms in integrated process planning and scheduling. J Intell Manuf 9(2):169–179

    Google Scholar 

  47. Rahmani K, Arezoo B (2007) A hybrid hint-based and graph-based framework for recognition of interacting milling features. Comput Ind 58:304–312

    Article  Google Scholar 

  48. Sakurai H (1995) Volume decomposition and feature recognition: part I—polyhedral objects. Comput Aided Des 27:833–843

    Article  Google Scholar 

  49. Sakurai, H, Chin C (1994) Definition and recognition of volume features for process planning, in Advances in Feature Based Manufacturing, Elsevier pp 65–80

  50. Sakurai H, Dave P (1996) Volume decomposition and feature recognition: part II—curved objects. Comput Aided Des 28:519–537

    Article  Google Scholar 

  51. Sridharan N, Shah JJ (2004) Recognition of multi axis milling features: part I—topological and geometric characteristics. J Comput Inform Sci Eng 4:242–250

    Article  Google Scholar 

  52. Tseng Y, Joshi SB (1994) Recognizing multiple interpretations of interacting machining features. Comput Aided Design 26(9):667–688

    Article  Google Scholar 

  53. Venkatadriagaram S (2000) Feature recognition and high-level process planning for rapid prototyping using 3-axis milling. University of California, Berkeley

  54. Vujosevic R, Kusiak A (1993) Selection of machinable volumes: an object-oriented approach. Expert Systems Appl 4:273–283

    Article  Google Scholar 

  55. Wong TN, Lam SM (2000) Automatic recognition of machining features from computer aided design part models. Proceeding of the Institution of Mechanical Engineers 515–520

  56. Woo Y, Sakurai H (2002) Recognition of maximal features by volume decomposition. Comput Aided Des 34:195–207

    Article  Google Scholar 

  57. Zhang C, Chan KW, Chen YH (1997) A method for recognizing feature interactions and feature components within the interactions. Int J Adv Manuf Tech 13:713–722

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Industrial Engineering Department, Sharif University of Technology, P.O. Box 11155-9414, Azadi Ave, Tehran, Iran

    Mahmoud Houshmand, Din Mohammad Imani & S. T. A. Niaki

Authors
  1. Mahmoud Houshmand
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Din Mohammad Imani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. S. T. A. Niaki
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mahmoud Houshmand.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Houshmand, M., Imani, D.M. & Niaki, S.T.A. Using flower pollinating with artificial bees (FPAB) technique to determine machinable volumes in process planning for prismatic parts. Int J Adv Manuf Technol 45, 944–957 (2009). https://doi.org/10.1007/s00170-009-2023-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-009-2023-x

Keywords

Search

Navigation