FUZZY LOGIC TEST IN DRAWING AND CALCULATING DEFECTIVE RATIO CONTROL CHARTS IN INDUSTRIES COMPANY

Salman Hussien  OMRAN; Salam Waley  Shneen; Batool Ibrahim  Jameel; Omar Hashim Hassoon; Fatimah Ridha  Abbood

doi:10.30572/2018/KJE/170102

Authors

Salman Hussien OMRAN Energy and Renewable Energies Technology Centre, University of Technology, Baghdad, Iraq
Salam Waley Shneen Energy and Renewable Energies Technology Centre, University of Technology, Baghdad, Iraq https://orcid.org/0000-0003-3718-6104
Batool Ibrahim Jameel University of Technology, Department of Production Engineering and Metallurgy, Industrial Engineering
Omar Hashim Hassoon University of Technology, Department of Production Engineering and Metallurgy, Industrial Engineering, Baghdad, Iraq
Fatimah Ridha Abbood Ministry of electricity, Baghdad, Iraq

DOI:

https://doi.org/10.30572/2018/KJE/170102

Keywords:

Statistical Process, Attributes Control, P-chart, Fuzzy Sets Theory, Ranking function

Abstract

For businesses to remain competitive in the market today, the quality of their products must either be improved upon or maintained. Therefore, creating a fresh strategy that might make more use of data from the production process has turned into a necessary program for every organization looking to boost quality. Fuzzy attribute control charts were created in the current study to track the manufacturing process. The triangle membership function was used to get the fuzzy numbers, and the recommended ranking function was then applied to turn them into a conventional sample. Fuzzy control charts have the potential to mitigate uncertainty stemming from incomplete, ambiguous, and/or confusing information, also the inherent uncertainty originating from measurement randomness in quality characteristics. Through data collection from the Al-Mamon facility and comparison with the conventional Shewhart control charts, a case study at the state corporation for vegetable oils in Iraq was used to validate the suggested fuzzy control charts. This study compares the use of fuzzy logic with the traditional way when adopting variable cases through the arrangement function to attain the control limits for faulty percentages and quality control for all samples using (w=0.6, 0.8, 0.9) and (λ = 0.7, 0.9). The results showed that a fuzzy control chart manages manufacturing quality more quickly, cheaply, and accurately. It makes it easier to find defective units during the manufacturing process, which helps to quickly ascertain whether or not production is under control. It also offers quality enhancements that are advantageous to the company

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