Reducing Uncertainty in a Type J Thermocouple Calibration Process

  • Yusuf Tansel İçEmail author
  • Ebru Saraloğlu Güler
  • Zeynep Erbil Çakır


Thermocouples are used in many manufacturing processes in order to read the actual temperature of the product. Calibration of thermocouples is critical wherever they are used. However, the uncertainties must be considered and the factors that affect the uncertainty value must be regarded during the calibration of thermocouples. In this study, design of experiments by Taguchi method has been performed in order to reduce uncertainty during calibration of Type J thermocouples. Within the scope of this study, parameters which are assumed to effect temperature oscillations have been determined and necessary experiments have been conducted using temperature well and proper inserts. The parameters were selected as insert material, thermocouple immersion depth and type. It can be concluded that the immersion type has the highest effect, whereas “immersion depth” has minimum effect on the uncertainty value. As a result of the study, a value for parameters which results in best possible temperature uniformity of the well is achieved.


Calibration Design of experiments Taguchi method Thermocouple Uncertainty 



  1. 1.
    Joint Committee for Guides in Metrology (JCGM), Evaluation of measurement data guide to the expression of uncertainty in measurement. Jt. Comm. Guid. Metrol. (2008). Retrieved from
  2. 2.
    K.D. Hill, D.J. Gee, Quantifying the calibration uncertainty attributable to thermocouple inhomogeneity. AIP Conf. Proc. 1552, 520–525 (2013)ADSCrossRefGoogle Scholar
  3. 3.
    J.V. Pearce, P.M. Harris, J.C. Greenwood, Evaluating uncertainties in interpolations between calibration data for thermocouples. Int. J. Thermophys. 31, 1517–1526 (2010)ADSCrossRefGoogle Scholar
  4. 4.
    D. Zvizdic, D. Serfezi, L.G. Bermanec, G. Bonnier, E. Renaot, Estimation of uncertainties in comparison calibration of thermocouples. Am. Inst. Phys. 7, 529–534 (2003)Google Scholar
  5. 5.
    C.W. Meyer, K.M. Garrity, Updated uncertainty budgets for NIST thermocouple calibrations. AIP Conf. Proc. 1552, 510–515 (2013)ADSCrossRefGoogle Scholar
  6. 6.
    A. Godina, T. Vuherer, B. Acko, Possibilities for minimising uncertainty of dissimilar materials gauge blocks calibration by mechanical comparison. Meas. J. Int. Meas. Confed. 45, 517–524 (2012)CrossRefGoogle Scholar
  7. 7.
    Y. Bitou, H. Hosoya, K. Mashimo, Uncertainty reducing method for the reference standards in gauge block comparator calibration. Meas. J. Int. Meas. Confed. 50, 293–296 (2014)CrossRefGoogle Scholar
  8. 8.
    R. Kadis, Evaluation of the measurement uncertainty: some common mistakes with a focus on the uncertainty from linear calibration. J. Chromatogr. A 1499, 226–229 (2017)CrossRefGoogle Scholar
  9. 9.
    D. Badocco, I. Lavagnini, A. Mondin, P. Pastore, Effect of multiple error sources on the calibration uncertainty. Food Chem. 177, 147–151 (2015)CrossRefGoogle Scholar
  10. 10.
    M.L.C.C. Reis, R.M. Castro, O.A.F. Mello, Calibration uncertainty estimation of a strain-gage external balance. Meas. J. Int. Meas. Confed. 46, 24–33 (2013)CrossRefGoogle Scholar
  11. 11.
    M.S. Phadke, Quality Engineering Using Robust Design (Prentice Hall, Englewood Cliffs, 1989)Google Scholar
  12. 12.
    F. Bayramov, C. Taşdemir, M.A. Taşdemir, Optimisation of steel fibre reinforced concretes by means of statistical response surface method. Cem. Concr. Compos. 26, 665–675 (2004)CrossRefGoogle Scholar
  13. 13.
    E.K.K. Nambiar, K. Ramamurthy, Models relating mixture composition to the density and strength of foam concrete using response surface methodology. Cem. Concr. Compos. 28, 752–760 (2006)CrossRefGoogle Scholar
  14. 14.
    S.H.A. Rahman, J.P. Choudhury, A.L. Ahmad, A.H. Kamaruddin, Optimization studies on acid hydrolysis of oil palm empty fruit bunch fiber for production of xylose. Bioresour. Technol. 98, 554–559 (2007)CrossRefGoogle Scholar
  15. 15.
    S.S. Habib, Study of the parameters in electrical discharge machining through response surface methodology approach. Appl. Math. Model. 33, 4397–4407 (2009)CrossRefGoogle Scholar
  16. 16.
    S. Fallah-Jamshidi, M. Amiri, Synergy of ICA and MCDM for multi-response optimisation problems. Int. J. Prod. Res. 51, 652–666 (2013)CrossRefGoogle Scholar
  17. 17.
    T. Yang, P. Chou, Solving a multiresponse simulation-optimization problem with discrete variables using a multiple-attribute decision-making method. Math. Comput. Simul. 68, 9–21 (2005)CrossRefGoogle Scholar
  18. 18.
    Y. Kuo, T. Yang, G.W. Huang, The use of a grey-based Taguchi method for optimizing multi-response simulation problems. Eng. Optim. 40, 517–528 (2008)CrossRefGoogle Scholar
  19. 19.
    P. Mandal, Signal-to-noise ratio: a fundamental and broad process performance measure. J. Eng. Des. 23, 924–941 (2012)CrossRefGoogle Scholar
  20. 20.
    Y.T. Ic, S. Yıldırım, MOORA-based Taguchi optimisation for improving product or process quality. Int. J. Prod. Res. 51, 3321–3341 (2013)CrossRefGoogle Scholar
  21. 21.
    B. Şimşek, Y.T. İç, E. Şimşek, A TOPSIS-based Taguchi optimization to determine optimal mixture proportions of the high strength self-compacting concrete. Chemom. Intell. Lab. Syst. 125, 18–32 (2013)CrossRefGoogle Scholar
  22. 22.
    J. Griggs, Measurement uncertainties, in Multi- Agency Radiological Laboratory-Marlab, vol. 19, pp. 1–30 (2004)Google Scholar
  23. 23.
    E. Sadıkhov, R. Kangı, S. Uğur, Measurement uncertainty (Metroloji Enstitüsü, Ulus, 1995)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Yusuf Tansel İç
    • 1
    Email author
  • Ebru Saraloğlu Güler
    • 2
  • Zeynep Erbil Çakır
    • 3
  1. 1.Department of Industrial Engineering, Faculty of EngineeringBaskent UniversityBaglica, EtimesgutTurkey
  2. 2.Department of Mechanical Engineering, Faculty of EngineeringBaskent UniversityBaglica, EtimesgutTurkey
  3. 3.Turkish Aerospace Industry Co.KahramankazanTurkey

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