Comparing Fuzzy Charts With Probability Charts And Using Them In A Textile Company

Abstract

In this article it has been tried to show that fuzzy theory performs better than probability theory in monitoring the product quality. A method that uses statistical techniques to monitor and control product quality is called statistical process control (SPC), where control charts are test tools frequently used for monitoring the manufacturing process. In this study, statistical quality control and the fuzzy set theory are aimed to combine. As known, fuzzy sets and fuzzy logic are powerful mathematical tools for modeling uncertain systems in industry, nature and humanity; and facilitators for common-sense reasoning in decision making in the absence of complete and precise information. In this basis for a textile firm for monitoring the yarn quality, control charts according to fuzzy theory by considering the quality in terms of grades of conformance as opposed to absolute conformance and nonconformance. And then with the same data for a textile factory, the control chart based on probability theory is constructed. The results of control charts based on two different approaches are compared. It's seen that fuzzy theory performs better than probability theory in monitoring the product quality.

Quality Variation Minimizer: A New Approach for Quality Improvement in Textile Industry

“…Mukhopadhyay and Ray (2006) applied Six Sigma to reduce yarn packing defects and they had the techniques of Six Sigma i.e., control chart, sigma level, MSA, regression, etc. Feili and Fekraty (2010) constructed the control charts on the basis of probability and fuzzy theory to monitor the yarn quality. They have found that fuzzy theory performs better than probability theory for monitoring product quality.…”