31 December 2008 Determining sample size in binary measurement system
Author Affiliations +
Proceedings Volume 7130, Fourth International Symposium on Precision Mechanical Measurements; 71305X (2008) https://doi.org/10.1117/12.819773
Event: Fourth International Symposium on Precision Mechanical Measurements, 2008, Anhui, China
Measurement system analysis guarantees the reliability of acquired data. Although much research has been performed regarding variable measurement system and Gage R&R has been comprehensively employed across many companies, there is relatively little attention that has been paid to binary measurement system, which is considered to be more practical and efficient. Proportion of agreement is generally utilised to evaluate binary measurement system in the traditional AIAG method. As a consequence, sample size should be more reasonably determined, in which process the number of parts, appraisers, and trials are of key importance. However, this critical issue has not been profoundly investigated as yet. In the present study, the number of parts is determined through the plot of length of confidence interval, and an alternative method is introduced to choose the number of appraisers and trials based on the majority voting rule. This is considered to be more sensible than the prevalent rule, in which two appraisers and two trials are usually chosen and an agreement is made only when the conclusions of both appraisers and trials are the same. In addition, a data set is analysed using the proposed method, and the results indicate that it is more rational.
© (2008) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yan Zhao, Yan Zhao, Zhen He, Zhen He, Liam Blunt, Liam Blunt, Xiangqian Jiang, Xiangqian Jiang, Yanlong Cao, Yanlong Cao, Hongyu Zhang, Hongyu Zhang, } "Determining sample size in binary measurement system", Proc. SPIE 7130, Fourth International Symposium on Precision Mechanical Measurements, 71305X (31 December 2008); doi: 10.1117/12.819773; https://doi.org/10.1117/12.819773


An intelligent diagnosis model based on rough set theory
Proceedings of SPIE (March 12 2013)
Complications in disassembly line balancing
Proceedings of SPIE (February 08 2001)
Intelligent Visual Inspection Machines
Proceedings of SPIE (December 18 1985)
Reliability test for phase-change optical recording media
Proceedings of SPIE (February 06 2001)

Back to Top