Skip to main content

Advertisement

SpringerLink
Log in

A novel process planning schema based on process knowledge customization

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

Abstract

Process planning, which requires a variety of knowledge of design and manufacturing, is still one of the most difficult issues in the research on computer-integrated manufacturing and also a bottleneck hampering the process of automation and digitalization of manufacturing. The basic crux of computer-aided process planning involves the diversity of manufacturing background, the complexity of decision rules, and the complexity of reasoning logic, which makes process planning increasingly difficult. To solve these problems, we address some primary issues of process planning in previous work, which involves decision logic of backward chaining reasoning, clustering, and meta-modeling for manufacturing resources. However, as of now, there is a lack of a systematic approach for process planning from the global viewpoint. The major objective of this research is to build a novel process planning schema based on process knowledge customization. The outcomes of this study lay the foundation for process planning in future rapid process preparation. Finally, an illustrative example is used to explain the novel process planning schema based on process knowledge customization.

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. Cay F, Chassapis C (1997) An IT view on perspectives of computer aided process planning research. Comput Ind 34(3):307–337

    Article  Google Scholar 

  2. Kolli S (2004) Classification of research and application in feature modeling and computer aided process planning. Master Thesis, Ohio University

  3. Mahmood F (1998) Computer aided process planning for wire electrical discharge machining (WEDM). Ph.D. Thesis, The Graduate Faculty of the School of Engineering, University of Pittsburgh

  4. Ramesh MM (2002) Feature based methods for machining process planning of automotive powertrain components. Ph.D. Thesis, Department of Mechanical Engineering, The University of Michigan

  5. 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 Intell Manuf 17:557–569

    Article  Google Scholar 

  6. Luttervelt KV (2003) Report on the CIRP working group CAPP. In: Proceedings of CIRP ECN-WG meeting. Paris, France

  7. Marefat M, Britanik J (1997) Case-based process planning using an object-oriented model representation. Robot Comput-Integr Manuf 13(3):229–251

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  10. You CF, Lin CH (2005) Java-based computer-aided process planning. Int J Adv Manuf Technol 26:1063–1070

    Article  Google Scholar 

  11. Tong YF, Li DB, Li CB, Yu MJ (2008) A feature-extraction-based process-planning system. Int J Adv Manuf Technol 38:1192–1200

    Article  Google Scholar 

  12. Kusiak A (1991) Process planning: a knowledge-based and optimization perspective. IEEE Trans Robot Autom 7(3):257–266

    Article  Google Scholar 

  13. Nau DS, Luce M (1987) Knowledge representation and reasoning techniques for process planning: extending SIPS to do tool selection. In: Proceedings of the 19th CIRP international seminar on manufacturing systems. Pennsylvania State University, USA, pp 91–98

    Google Scholar 

  14. Wang HP, Li JK (1991) Computer-aided process planning. Elsevier Science, New York

    Google Scholar 

  15. David BA, Wu J (1999) Analogy-based multiple process planning system with resource conflicts. Int J Flex Manuf Syst 11:63–82

    Article  Google Scholar 

  16. Tiwari MK, Kotaiah KR, Bhatnagar S (2001) A case-based computer-aided process planning system for machining prismatic components. Int J Adv Manuf Technol 17:400–411

    Article  Google Scholar 

  17. Kumar M, Rajotia S (2003) Integration of scheduling with computer aided process planning. J Mater Process Technol 138:297–300

    Article  Google Scholar 

  18. Saygin C, Kilic SE (1999) Integrating flexible process plans with scheduling in flexible manufacturing systems. Int J Adv Manuf Technol 15:268–280

    Article  Google Scholar 

  19. Liu D, Duan G, Lei N, Wang JS (1999) Analytic hierarchy process based decision modeling in CAPP development tools. Int J Adv Manuf Technol 15(1):26–31

    Article  Google Scholar 

  20. Xu HM, Mi LC, Li DB (2007) A decision logic schema of process planning using backward chaining reasoning. Int J Adv Manuf Technol 38(11–12):1181–1191

    Google Scholar 

  21. Xu HM, Li DB (2008) A clustering-based modeling scheme of the manufacturing resources for process planning. Int J Adv Manuf Technol 38(1–2):154–162

    Article  Google Scholar 

  22. Xu HM, Li DB (2008) A meta-modeling paradigm of the manufacturing resources using mathematical logic for process planning. Int J Adv Manuf Technol 36(9–10):1022–1031

    Article  Google Scholar 

  23. Xu HM, Li DB (2008) Modeling of process parameter selection with mathematical logic for process planning. Robot Comput Integr Manuf (in press). doi:10.1016/j.rcim.2008.03.001

  24. Chang TC (1985) Expert process planning for manufacturing. Addison-Wesley, Longman Austin

    Google Scholar 

  25. Chang TC, Wysk RA, Wang HP (2006) Computer-aided manufacturing, 3rd edn. Prentice Hall, Englewood Cliffs

    Google Scholar 

  26. Wright PK (2002) 21st Century manufacturing. University Press, Beijing

    Google Scholar 

  27. Russell SJ, Norvig P (2003) Artificial intelligence: a modern approach. Prentice Hall, Englewood Cliffs

    Google Scholar 

  28. Yang KH, Olson D, Kim J (2004) Comparison of first order predicate logic, fuzzy logic and non-monotonic logic as knowledge representation methodology. Expert Syst Appl 27:501–519

    MATH  Google Scholar 

  29. Meng SN, Shen YZ, et al (1991) Mechanical machining process manual, vol 1–2. China Machine Press, Beijing

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Mechanical and Electrical Engineering, HoHai University, Changzhou, Jiangsu, 213022, People’s Republic of China

    Huan-Min Xu & Ming-Hai Yuan

  2. School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing, 210094, People’s Republic of China

    Dong-Bo Li

Authors
  1. Huan-Min Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ming-Hai Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dong-Bo Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Huan-Min Xu.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Xu, HM., Yuan, MH. & Li, DB. A novel process planning schema based on process knowledge customization. Int J Adv Manuf Technol 44, 161–172 (2009). https://doi.org/10.1007/s00170-008-1804-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-008-1804-y

Keywords

Search

Navigation