Correction of the Popular Bias Test Method Recommended by ISO/TS16949 for Manufacturing Industries

Abstract:

Article Preview

Measurement systems play important roles in producing consistent, high quality products in manufacturing industries. An important requirement for a measurement system is the consistency or stability of its measurement results. The measurement bias needs to be checked periodically in production to decide whether the measurement system needs to be recalibrated timely to maintain the consistency. A very popular conventional method of statistical bias test recommended by ISO/TS16949 for manufacturing industries has been reviewed. Its flaws and problems are pointed out and illustrated with special examples. A new method of engineering bias test and its equivalent accuracy test have been proposed to correct the problems of the conventional statistical bias test.

Info:

Periodical:

Edited by:

Prasad Yarlagadda and Yun-Hae Kim

Pages:

131-136

Citation:

S. Y. F. Yang, "Correction of the Popular Bias Test Method Recommended by ISO/TS16949 for Manufacturing Industries", Applied Mechanics and Materials, Vols. 568-570, pp. 131-136, 2014

Online since:

June 2014

Export:

Price:

$41.00

[1] C.P. Kartha, (2004). A comparison of ISO 9000: 2000 quality system standards, QS9000, ISO/TS 16949 and Baldrige criteria,. The TQM Magazine Volume 16 (Emerald Group Publishing Limited). Number 5: 336.

DOI: https://doi.org/10.1108/09544780410551269

[2] AIAG (Automotive Industry Action Group), Measurement Systems Analysis Manual, Fourth Edition, (2010).

[3] Bela G. Liptak edited, Instrument Engineers' Handbook, Vol. 1: Process Measurement and Analysis; Fourth Edition, (2003).

DOI: https://doi.org/10.1201/9781420064025

[4] NCSL International, American National Standard for Calibration U.S. Guide to the Expression of Uncertainty in Measurement, ANSI/NCSL Z540-2-(1997).

[5] Barry N. Taylor and Christ E. Kuyatt, NIST Technical Note 1297, 1997 Edition, Guidelines for Evaluating and Expressing the Uncertainty of NIST Measurement Results. (1994).

DOI: https://doi.org/10.6028/nist.tn.1297

[6] Douglas C. Montgomery, Introduction to Statistical Quality Control, Seventh Edition, (2012).

[7] Fred V. Brock, Scott J. Richardson, Meteorological Measurement Systems, (2001).