Fuzzy Gauge Capability (Cg and Cgk) through Buckley Approach
Different terms of the Statistical Process Control (SPC)
has sketch in the fuzzy environment. However, Measurement System
Analysis (MSA), as a main branch of the SPC, is rarely investigated
in fuzzy area. This procedure assesses the suitability of the data to be
used in later stages or decisions of the SPC. Therefore, this research
focuses on some important measures of MSA and through a new
method introduces the measures in fuzzy environment. In this
method, which works based on Buckley approach, imprecision and
vagueness nature of the real world measurement are considered
simultaneously. To do so, fuzzy version of the gauge capability (Cg
and Cgk) are introduced. The method is also explained through
example clearly.
[1] Automotive Industry Action Group (AIAG), Measurement Systems
Analysis Reference Manual. 3rd ed., Chrysler, Ford, General Motors
Supplier Quality Requirements Task Force, 2002.
[2] J. M. Juran, F. M. Gyrna, Quality Planning and Analysis, McGraw-Hill,
New York, 1993.
[3] S. Senol, Measurement system analysis using designed experiments with
minimum α-β Risks and n. Measurement, 36, 131–141, 2004.
[4] H. T. Lee, Cpk index estimation using fuzzy numbers. European Journal
of Operational Research, 129, 683-688, 2001.
[5] P. K. Leung, F. Spiring, Adjusted action limits for Cpm based on
departures from normality. International Journal of Production
Economics, 107, 237-249, 2007.
[6] A. Parchami, M. Mashinchi, A.R. Yavari, and H.R. Maleki, Process
Capability Indices as Fuzzy Numbers. Austrian Journal of Statistics,
34(4), 391–402, 2005.
[7] A. Parchami, M. Mashinchi. Fuzzy estimation for process capability
indices. Information Sciences, 177, 1452–1462, 2007.
[8] J.J. Buckley, Elementary Queuing Theory based on Possibility Theory.
Fuzzy Sets and Systems37 1990, pp. 43–52.
[9] J.J. Buckley, Y. Qu, (1990), On Using α -Cuts to Evaluate Fuzzy
Equations. Fuzzy Sets and Systems 38,309–312.
[10] J.J. Buckley, T. Feuring, Y. Hayashi, (2001), Fuzzy Queuing Theory
Revisited. International Journal of Uncertainty, Fuzziness, and
Knowledge-based Systems 9,527–537.
[11] J.J. Buckley, E. Eslami, Uncertain Probabilities Ι: The Discrete Case;
Soft Computing 2003a, pp. 500-505.
[12] J.J. Buckley, E. Eslami, Uncertain Probabilities Π: The Continuous
Case; Soft Computing 2003b, pp. 500-505.
[13] J.J. Buckley, Fuzzy Statistics: Hypothesis Testing; Soft Computing,
2005a, (9) pp. 512-518.
[1] Automotive Industry Action Group (AIAG), Measurement Systems
Analysis Reference Manual. 3rd ed., Chrysler, Ford, General Motors
Supplier Quality Requirements Task Force, 2002.
[2] J. M. Juran, F. M. Gyrna, Quality Planning and Analysis, McGraw-Hill,
New York, 1993.
[3] S. Senol, Measurement system analysis using designed experiments with
minimum α-β Risks and n. Measurement, 36, 131–141, 2004.
[4] H. T. Lee, Cpk index estimation using fuzzy numbers. European Journal
of Operational Research, 129, 683-688, 2001.
[5] P. K. Leung, F. Spiring, Adjusted action limits for Cpm based on
departures from normality. International Journal of Production
Economics, 107, 237-249, 2007.
[6] A. Parchami, M. Mashinchi, A.R. Yavari, and H.R. Maleki, Process
Capability Indices as Fuzzy Numbers. Austrian Journal of Statistics,
34(4), 391–402, 2005.
[7] A. Parchami, M. Mashinchi. Fuzzy estimation for process capability
indices. Information Sciences, 177, 1452–1462, 2007.
[8] J.J. Buckley, Elementary Queuing Theory based on Possibility Theory.
Fuzzy Sets and Systems37 1990, pp. 43–52.
[9] J.J. Buckley, Y. Qu, (1990), On Using α -Cuts to Evaluate Fuzzy
Equations. Fuzzy Sets and Systems 38,309–312.
[10] J.J. Buckley, T. Feuring, Y. Hayashi, (2001), Fuzzy Queuing Theory
Revisited. International Journal of Uncertainty, Fuzziness, and
Knowledge-based Systems 9,527–537.
[11] J.J. Buckley, E. Eslami, Uncertain Probabilities Ι: The Discrete Case;
Soft Computing 2003a, pp. 500-505.
[12] J.J. Buckley, E. Eslami, Uncertain Probabilities Π: The Continuous
Case; Soft Computing 2003b, pp. 500-505.
[13] J.J. Buckley, Fuzzy Statistics: Hypothesis Testing; Soft Computing,
2005a, (9) pp. 512-518.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:70709", author = "Seyed Habib A. Rahmati and Mohsen Sadegh Amalnick", title = "Fuzzy Gauge Capability (Cg and Cgk) through Buckley Approach", abstract = "Different terms of the Statistical Process Control (SPC)
has sketch in the fuzzy environment. However, Measurement System
Analysis (MSA), as a main branch of the SPC, is rarely investigated
in fuzzy area. This procedure assesses the suitability of the data to be
used in later stages or decisions of the SPC. Therefore, this research
focuses on some important measures of MSA and through a new
method introduces the measures in fuzzy environment. In this
method, which works based on Buckley approach, imprecision and
vagueness nature of the real world measurement are considered
simultaneously. To do so, fuzzy version of the gauge capability (Cg
and Cgk) are introduced. The method is also explained through
example clearly.", keywords = "SPC, MSA, gauge capability, Cg, Cgk.", volume = "9", number = "8", pages = "1530-5", }