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Use Fuzzy Midrange Transformation Method to Construction Fuzzy Control Charts limits
Kawa M. Jamal Rashid, Suzan S. Haydar
Pages - 1 - 14     |    Revised - 31-12-2014     |    Published - 31-1-2015
Volume - 6   Issue - 1    |    Publication Date - January 2015  Table of Contents
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KEYWORDS
Statistical Process Control, Fuzzy Number, Fuzzy Control Charts, Membership Function, ? -cut and ? - Level Fuzzy Midrange.
ABSTRACT
Statistical Process Control (SPC) is approach that uses statistical techniques to monitor the process. The techniques of quality control are widely used in controlling any kinds of processes. The widely used control charts are R X ? and S X ? charts. These are called traditional variable control chart, which consists of three horizontal lines called Centre Line (CL), Upper Control Limit (UCL) and Lower Control Limit (UCL) are represented by numeric values. The center line in a control chart denotes the average value of the quality characteristic under study. A process is either "in control" or "out of control" depending on numeric observation values. In the consideration of real production process, it is assumed that there are no doubts about observations and their values. But when these observations include human judgments, evaluations and decisions, a continuous random variable (xi) of a production process should include the variability caused by human subjectivity or measurement devices, or environmental conditions. So, linguistic terms can be used instead of an exact value of continuous random variable. In this context fuzzy set theory is useful tool to handle this uncertainty. Numeric control limits can be transformed to fuzzy control limits by using membership function, therefore; the concept of fuzzy control charts with ? cuts by using ? -level fuzzy midrange with trapezoidal fuzzy number (TRN) is proposed. The fuzzy control charts for arithmetic mean ( X ~ ), and range (R ~ ) are developed. Fuzzy control limits provide a more accurate and flexible evaluation. In this paper through a real illustrative data from Sulaimani Company for Cement in the city of Sulaimani, shows the designing of fuzzy control chart for process average of quality.
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Dr. Kawa M. Jamal Rashid
School of Administrations and Economics/ Department of Statistics / University of Sulaimani - Iraq
kawa.jamal_57@yahoo.com
Dr. Suzan S. Haydar
School of Administrations and Economics/ Department of Statistics Sulamani University Sulamani, Iraq - Iraq