Skip to main content
SpringerLink
Log in

Efficient inspection planning for coordinate measuring machines

  • Original Article
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

The coordinate measuring machine (CMM) has been recognized as a powerful tool for dimensional and geometric tolerance inspection in the manufacturing industry. The power of the CMM depends heavily on an efficient inspection plan that measures a part in minimal time. This paper proposes CMM inspection planning that can minimize the number of part setups and probe orientations and the inspection feature sequence. In our planning, a greedy heuristic method is adopted to obtain the minimal number of part setups and probe changes. Meanwhile, a continuous Hopfield neural network is developed to solve the inspection feature-sequencing problem. The proposed method was successfully implemented and tested using a machine spindle cover part. The results show that the proposed method can achieve excellent performance compared to the other methods.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Schaffer (1982) Taking the measure of CMMs. Am Mach 145–160

  2. Wang N, Ozsoy TM (1991) A scheme to represent features, dimensions, and tolerances in geometric modeling. J Manuf Syst 10:233–240

    Google Scholar 

  3. ElMaraghy HA, Gu P (1987) Expert system for inspection process planning. Annals CIRP 36:85–89

    Google Scholar 

  4. Menq CH, Yau HT, Wong CL (1992) An intelligent planning environment for automated dimensional inspection using coordinate measuring machines. Trans ASME 144:222–230

    Google Scholar 

  5. Merat FL, Radack GM (1992) Automatic inspection planning within a feature-based cad system. Robot Comput Integr Manuf 9:61–69

    Article  Google Scholar 

  6. Roy U, Xu Y, Wang L (1994) Development of an intelligent inspection planning system in an object oriented programming environment. Comput Integr Manuf Syst 7:240–246

    Article  Google Scholar 

  7. Gu P, Chan K (1995) An integrated object-oriented product modeling and inspection planning system. J Design Manuf 5:127–141

    Google Scholar 

  8. Lin Z-C, Chen C-C (1997) Measuring-sequence planning by the nearest neighbor method and the refinement method. Int J Adv Manuf Technol 13:271–281

    Google Scholar 

  9. Ziemian CW, Medeiros DJ (1998) Automating probe selection and part setup planning for inspection on a coordinate measuring machine. Int J Comput Integr Manuf 11:448–460

    Article  Google Scholar 

  10. Fan K-C, Leu MC (1998) Intelligent planning of CAD-directed inspection for coordinate measuring machines. Comput Integr Manuf Syst 11:43–51

    Article  Google Scholar 

  11. Kweon S, Medeiros DJ (1998) Part orientations for CMM inspection using dimensioned visibility maps. CAD 30:741–749

    Article  Google Scholar 

  12. Lin Z-C, Chow J-J (2001) Near optimal measuring sequence planning and collision-free path planning with a dynamic programming method. Int J Adv Manuf Technol 18:29–43

    Article  Google Scholar 

  13. Beg J, Shunmugam MS (2002) An object oriented planner for inspection of prismatic parts—OOPIPP. Int J Adv Manuf Technol 19:905–916

    Article  Google Scholar 

  14. Aho AV, Hopcroft JE, Ullman JD (1972) The design and analysis of computer algorithms. Addison-Wesley, Reading

  15. Chvátal V (1979) A greedy heuristic for the set-covering problem. Math Oper Res 5:233–235

    Google Scholar 

  16. Corman TH, Leiserson CE, Rivest RL (1989) Introduction to Algorithms. MIT, Cambridge 974–978

  17. Hopfield JJ, Tank DW (1985) Neural computation of decision in optimization problems. Biol Cybern 52:141–152

    CAS  PubMed  Google Scholar 

  18. Abe S, Kawakami J, Hirasawa K (1992) Solving inequality constrained combinatorial optimization problems by the Hopfield neural networks. Neural Netw 5:663–670

    Google Scholar 

  19. Wilson GV, Pawley GS (1988) On the stability of the traveling salesman problem algorithm of Hopfield and Tank. Biol Cybern 58:63–70

    CAS  PubMed  Google Scholar 

  20. Takefuji Y (1992) Neural network parallel computing. Kluwer, Boston

  21. Corrigall MJ, Bell R (1991) Probe and component set-up planning for coordinate measuring machines. Int J Comput Integr Manuf 4:34–44

    Google Scholar 

  22. Aiyer SVB, Niranjan M, Fallside F (1990) A theoretical investigation into the performance of the Hopfield model. IEEE Trans Neural Netw 1:204–215

    Article  Google Scholar 

  23. Kamger-Parsi B, Kamger-Parsi B (1992) Hopfield and optimization problems. In: Neural networks perception-computation, learning, and architectures. Academic Press, San Diego

Download references

Author information

Authors and Affiliations

  1. SANYES Automotive Industry, Tainan Hsien, Taiwan

    C.-Y. Hwang

  2. Department of Industrial Engineering and Management, Yuan-Ze University, 135 Yuan-Tung Rd., Chung-Li, Taoyuan 320, Taiwan

    C.-Y. Tsai

  3. Department of Industrial and Manufacturing System Engineering, University of Missouri-Columbia, E3437G Engineering Building East, Columbia, MO 65211, USA

    C. A. Chang

Authors
  1. C.-Y. Hwang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. C.-Y. Tsai
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. C. A. Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to C.-Y. Tsai.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Hwang, CY., Tsai, CY. & Chang, C.A. Efficient inspection planning for coordinate measuring machines. Int J Adv Manuf Technol 23, 732–742 (2004). https://doi.org/10.1007/s00170-003-1642-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-003-1642-x

Keywords

Search

Navigation