Skip to main content
SpringerLink
Log in

Geometric Tolerance Evaluation Using Combined Vision – Contact Techniques and Other Data Fusion Approaches

  • Chapter
Geometric Tolerances

Abstract

The development of ever-pressing requirements for geometric tolerances has produced two main measuring needs: to obtain the geometric values of industrial products with higher precision and to obtain these values in a reduced time span. In order to accomplish these objectives, one of the most investigated and applied approaches is the use of multiple sensors on a traditional coordinate measuring machine (CMM). The resulting machine is usually referred to as a hybrid CMM and it is able to combine the data from optical and contact sensors in order to produce the measurement of a specific object with higher precision and in less time with respect to the traditional CMM approach. This chapter briefly explains the hybrid CMM characteristics and the working principles of the most used sensors. Then the method for the treatment of the data acquired by the multiple sensors is presented, starting from the basic problem of data registration to the algorithm to integrate and fuse the optical and touch probe data.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Besl P, McKay N (1992) A method for registration of 3-D shapes. IEEE Trans Pattern Anal Mach Intell 14:239–256

    Article  Google Scholar 

  • Carbone V, Carocci M, Savio E, Sansoni G, De Chiffre L (2001) Combination of a vision system and a coordinate measuring machine for the reverse engineering of freeform surfaces. Int J of Adv Manuf Technol 17:263–271

    Article  Google Scholar 

  • Cernuschi-Frias B (1984) Orientation and location parameter estimation of quadric surfaces in 3D space from a sequence of images. PhD dissertation, Brown University of Providence

    Google Scholar 

  • Chan V, Bradley C, Vickers W (1997) Automating laser scanning of 3-D surfaces for reverse engineering. SPIE 3204:156–164

    Article  Google Scholar 

  • Chan VH, Bradley C, Vickers GW (2001) A multi-sensor approach to automating co-ordinate measuring machine-based reverse engineering. Comput Ind 44:105–115

    Article  Google Scholar 

  • Chen LC, Lin GCI (1991) A vision-aided reverse engineering approach to reconstructing free-form surfaces. Robot Comput Integr Manuf 13(4):323–336

    Article  Google Scholar 

  • Chen LC, Lin GCI (1997) Reverse engineering of physical models employing a sensor integration between 3D stereo detection and contact digitization. Proc SPIE 3204:146–155

    Article  Google Scholar 

  • Chen YM, Huang HC (2000) Fuzzy logic approach to multisensor data association. Math Comput Simul 52:399–412

    Article  Google Scholar 

  • Chen X, Xia J, Jin Y, Sun J (2009) Accurate calibration for a camera–projector measurement system based on structured light projection. Opt Lasers Eng 47(3–4):310–319

    Article  Google Scholar 

  • Chetverikov D, Stepanov D, Krsek P (2005) Robust Euclidean alignment of 3D point sets: the trimmed iterative closest point algorithm. Image Vision Comp 23:299–309

    Article  Google Scholar 

  • Daniels J, Ha HK, Ochotta T, Silva CT (2007) Robust smooth feature extraction from point clouds. In: Proceedings of IEEE international conference on shape modeling and applications

    Google Scholar 

  • El-Hakim SF, Pizzi N (1993) Multicamera vision-based approach to flexible feature measurement for inspection and reverse engineering. Opt Eng 32:2201–2215

    Article  Google Scholar 

  • Ferreira M, Higdon D, Lee H (2005) Multi-scale random field models. ISDS discussion paper 05-02. http://ftp.stat.duke.edu/WorkingPapers/05-02.html

    Google Scholar 

  • Forest J, Salvi J (2002) A review of laser scanning three dimensional digitizers. Intell Robot Syst 73–78

    Google Scholar 

  • Fujimoto M, Kadya K (1993) An improved method for digitised data reduction using an angle parameter. Measurement 12:113–122

    Article  Google Scholar 

  • Hornbeck LJ (1998) From cathode rays to digital micromirrors: a history of electronic projection display technology. Texas Instruments Technol J 15(3):7–40

    Google Scholar 

  • Huang J, Menq CH (1999) Feature extraction from incomplete surface data. Coordinate Metrology Measurement Laboratory technical report CMML-99-03

    Google Scholar 

  • Huang YB, Lan YB, Hoffmann WC, Lacey RE (2007) Multisensor data fusion for high quality data analysis and processing in measurement and instrumentation. J Bionic Eng 4:53–62

    Article  Google Scholar 

  • Kennedy M, O’Hagan A (2001) Bayesian calibration of computer models. J R Stat Soc Ser B 63:425–464

    Article  MATH  MathSciNet  Google Scholar 

  • Lee N L, Menq CH (1995) Automated recognition of geometric forms from B-rep models. In: Proceedings of ASME international computers in engineering conference, pp 805–816

    Google Scholar 

  • Pulli K (1997) Surface reconstruction and display from range and color data. PhD thesis, University of Washington

    Google Scholar 

  • Qian Z, Wu CFJ (2006) Bayesian hierarchical modeling for integrating low-accuracy and high-accuracy experiments. Technometrics 50(2):192–204

    Article  MathSciNet  Google Scholar 

  • Rousseeuw P, Van Zomeren B (1990) Unmasking multivariate outliers and leverage points. J Am Stat Assoc 85:633–651

    Article  Google Scholar 

  • Nashman M (1993) The use of vision and touch sensors for dimensional inspection tasks. Manuf Rev 6(2):155–162

    Google Scholar 

  • Nashman M, Hong TH, Rippey WG, Herman M (1996) An integrated vision touch-probe system for dimensional inspection tasks. In: Proceedings of SME Applied Machine Vision ’96 conference, Cincinnati, pp 243–255

    Google Scholar 

  • Shen TS, Huang J, Menq CS (2000) Multiple-sensor integration for rapid and high-precision coordinate metrology. IEEE/ASME Trans Mechatronics 5(2):110–122

    Article  Google Scholar 

  • Spitz SN, Spyridi AJ, Requicha AAG (1999) Accessibility analysis for planning of dimensional inspection with coordinate measuring machines. IEEE Trans Robot Automat 15(4):714–727

    Article  Google Scholar 

  • Weng J, Cohen P, Herniou M (1992) Camera calibration with distortion models and accuracy evaluation. IEEE Trans Pattern Anal Machine Intell 14:965–980

    Article  Google Scholar 

  • Xia H, Ding Y, Mallicky BK (2008a) Bayesian hierarchical model for integrating multi-resolution metrology data. In: Proceedings of European Network for Business and Industrial Statistics conference

    Google Scholar 

  • Xia H, Ding Y, Wang J (2008b) Gaussian process method for form error assessment using coordinate measurements. IIE Trans 40(10):931–946

    Article  Google Scholar 

  • Yang J, Lee NL, Menq CH (1995) Application of computer vision in reverse engineering for 3D coordinate acquisition. In: Proceedings of the symposium on concurrent product and process engineering, ASME international mechanical engineering congress and exposition, pp 143–156

    Google Scholar 

  • Yau HT, Menq CH (1996) A unified least-squares approach to the evaluation of geometric errors using discrete measurement data. Int J Mach Tools Manuf 36(11):1269–1290

    Article  Google Scholar 

  • Zexiao X, Jianguo W, Ming J (2007) Study on a full field of view laser scanning system. Int J Mach Tools Manuf 47:33–43

    Article  Google Scholar 

  • Zussman E, Schuler H, Seliger G (1994) Analysis of the geometrical feature detectability constraints for laser-scanner sensor planning. Int J Adv Manuf Technol 9:56–64

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dipartimento di Meccanica e Tecnologie Industriali, Università di Firenze, Via di S. Marta 3, 50139, Florence, Italy

    Gianni Campatelli

Authors
  1. Gianni Campatelli
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Politecnico di Milano, Via La Masa 1, 20156, Milano, Italy

    Bianca M. Colosimo

  2. University of Perugia, Via Duranti 67, 06125, Perugia, Italy

    Nicola Senin

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag London Limited

About this chapter

Cite this chapter

Campatelli, G. (2011). Geometric Tolerance Evaluation Using Combined Vision – Contact Techniques and Other Data Fusion Approaches. In: Colosimo, B., Senin, N. (eds) Geometric Tolerances. Springer, London. https://doi.org/10.1007/978-1-84996-311-4_6

Download citation

  • DOI: https://doi.org/10.1007/978-1-84996-311-4_6

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84996-310-7

  • Online ISBN: 978-1-84996-311-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation