Comparative Traditional Methods of Attributes with Fuzzy Quality Control Charts for Improving the Quality of a Product
DOI:
https://doi.org/10.59247/jfsc.v3i1.273Keywords:
Fuzzy Logic, Quality Control Charts, Attribute Control Charts, Statistical Quality ControlAbstract
The problems motivating the study, as a result of sudden changes in production quality levels, which affect the production process. Control charts are a major tool in statistical quality control. The aim of the study is to monitor the production quality of a product that is an engine used in the application of a hair dryer. The methodology followed, the hair dryer model was chosen to verify the possibility of improving the product quality using fuzzy logic and comparing the traditional Shewhart control charts (p-chart) with the fuzzy p-chart in the context of manufacturing, and the collected data were processed using Minitab 21 Statistical Software. The performance of a control chart using fuzzy logic was measured for the proposed industrial product type with specifications for 300 samples of the constant size and a production period of 25 days to identify the product quality. The basic criterion for drawing the chart using fuzzy logic depends on the fuzzy ordering function for each of w, λ and its values are within the limits of (0 < λ or w ≤ 1) is a weighting parameter. The necessary tests were conducted to monitor the product quality using (w = 0.1, 0.2) and (λ = 0.1, 0.2) when the fuzzy ordering function is used. Results, it was found that the fuzzy p-chart was more sensitive to process changes and could detect shifts in defect ratio faster and more accurately, the production process was under statistical control and within quality control limits, and the conventional deviation from nominal control charts showed a false alarm for the observation as out of control. Recommendations, the present method can be used to improve product quality and reduce defects for the motor department.
References
Xiao, Qing, and Guangjun Li. "Application and challenges of statistical process control in radiation therapy quality assurance." International Journal of Radiation Oncology* Biology* Physics, vol. 118, no. 1 pp. 295-305, 2024, https://doi.org/10.1016/j.ijrobp.2023.08.020.
Qamar, S. Z., Al-Hinai, N., & Márquez, F. P. G. (2024). Quality Control and Quality Assurance: Techniques and Applications. BoD–Books on Demand. 2024. https://books.google.co.id/books?hl=id&lr=&id=0jEREQAAQBAJ.
F. A. Ahmed, “Study of Statistical Control Charts and Process Capability to Improve Quality Control Techniques (Case Study Applied in Wool Textile Company–Baniwalid),” Sirte University Scientific Journal, vol. 5, no. 1, pp. 01-22, 2015, https://journal.su.edu.ly/index.php/susj/article/view/1110.
A. B. Abdulghafour, S. H. Omran, M. S. Jafar, M. M. Mottar, and O. H. Hussein, “Application of statistical control charts for monitoring the textile yarn quality,” In Journal of Physics: Conference Series, vol. 1973, no. 1, p. 012158, 2021, https://doi.org/10.1088/1742-6596/1973/1/012158.
B. Mason and J. Antony, “Statistical process control: an essential ingredient for improving service and manufacuring quality,” Managing Service Quality: An International Journal, vol. 10, no. 4, pp. 233-238, 2000, https://doi.org/10.1108/09604520010341618.
A. Mitra. Fundamentals of quality control and improvement. John Wiley & Sons. 2016. https://books.google.co.id/books?hl=id&lr=&id=OqjLCgAAQBAJ.
R. R. Jabbar and A. A. A. Alkhafaji, “Analysis of Traditional and Fuzzy Quality Control Charts to Improve Short-Run Production in the Manufacturing Industry,” Journal of Engineering, 29(6), 159-176, 2023, https://doi.org/10.31026/j.eng.2023.06.12.
S. H. Omran, S. W. Shneen, and M. M. AL-Khafaji, “Beta-Deficient Estimators with Truncated Sampling Plan in Quality Control for the Auto Battery Industry,” Journal of Fuzzy Systems and Control, vol. 2, no. 2, pp. 81-86, 2024, https://doi.org/10.59247/jfsc.v2i2.181.
سلمان حسين عمران, “Statistical Quality Control of Industrial Products at the General Company for Vegetable Oils,” Journal of Engineering, vol. 18, no. 06, pp. 135-149, 2012, https://doi.org/10.31026/j.eng.2012.06.08.
O. H. Hassoon, B. Ibrahim, and B. Albaghdadi, “Building A Computerize System for Controlling and Monitoring Manufacturing Operations Based on Statistical Quality Control,” In IOP Conference Series: Materials Science and Engineering, vol. 881, no. 1, p. 012064, 2020, https://doi.org/10.1088/1757-899X/881/1/012064.
M. A. Raheem, A. T. Gbolahan, and I. E. Udoada, “Application of statistical process control in a production process,” Science journal of applied mathematics and statistics, vol. 4, no. 1, pp. 1-11, 2016, https://doi.org/10.11648/j.sjams.20160401.11.
T. Nandedkar and G. Bhati, “Assessment of Academic Performance through SQC: An Application of Control Charts,” In Interdisciplinary Research in Technology and Management, pp. 76-81, 2021, https://doi.org/10.1201/9781003202240-13.
İ. Kaya, E. İlbahar, and A. Karaşan, “A design methodology based on two dimensional fuzzy linguistic variables for attribute control charts with real case applications,” Engineering Applications of Artificial Intelligence, vol. 126, p. 106792, 2023, https://doi.org/10.1016/j.engappai.2023.106792.
N. P. Alakoc and A. Apaydin, “A Fuzzy Control Chart Approach for Attributes and Variables,” Engineering, Technology & Applied Science Research, vol. 8, no. 5, 2018, https://doi.org/10.48084/etasr.2192.
J. C. Benneyan, “Statistical quality control methods in infection control and hospital epidemiology, part I introduction and basic theory,” Infection Control & Hospital Epidemiology, vol. 19, no. 3, pp. 194-214, 1998, https://doi.org/10.2307/30143442.
S. H. G. Haridy. Attribute control charts for effective statistical process control (Doctoral dissertation), 2014, https://hdl.handle.net/10356/59545.
I. Dzitac, F. G. Filip, and M. J. Manolescu, “Fuzzy logic is not fuzzy: World-renowned computer scientist Lotfi A. Zadeh,” International Journal of Computers Communications & Control, vol. 12, no. 6, pp. 748-789, 2017, https://doi.org/10.15837/ijccc.2017.6.3111.
L. A. Zadeh. Fuzzy sets. Information and Control. 1965. https://doi.org/10.1016/S0019-9958(65)90241-X.
G. Bojadziev and M. Bojadziev. Fuzzy sets, fuzzy logic, applications. vol. 5. World scientific. 1995. https://books.google.co.id/books?hl=id&lr=&id=At2xJDcu-3sC.
Y. A. Enaya, A. A. Karim, S. M. Saleh, and S. W. Shneen, “Adapting Wired TCP for Wireless Ad-hoc Networks Using Fuzzy Logic Control,” Journal Européen des Systèmes Automatisés, vol. 57, no. 5, p. 1377, 2024, https://doi.org/10.18280/jesa.570513.
S. W. Shneen, J. M. D. Alkhasraji, and M. Q. Sulttan, “Internet-based Control of Thermo-optical Plant Improvement based on the PID-GWO System,” Journal of Fuzzy Systems and Control, vol. 2, no. 3, pp. 197-202, 2024, https://doi.org/10.59247/jfsc.v2i3.257.
A. L. Shuraiji and S. W. Shneen, “Fuzzy Logic Control and PID Controller for Brushless Permanent Magnetic Direct Current Motor: A Comparative Study,” Journal of Robotics and Control (JRC), vol. 3, no. 6, pp. 762-768, 2022, https://doi.org/10.18196/jrc.v3i6.15974.
N. D. Singpurwalla and J. M. Booker, “Membership functions and probability measures of fuzzy sets,” Journal of the American statistical association, vol. 99, no. 467, pp. 867-877, 2004, https://doi.org/10.1198/016214504000001196.
S. H. Omran, M. H. Ali, and M. A. Hajer, “Investigation of Medical Image Segmentation Using Machine Learning Based Fuzzy Reliability Function for MIoT,” In Journal of Physics: Conference Series, vol. 1501, no. 1, p. 012016, 2020, https://doi.org/10.1088/1742-6596/1501/1/012016.
Z. A. Al-Dabbagh and S. W. Shneen, “Neuro-Fuzzy Controller for a Non-Linear Power Electronic DC-DC Boost Converters,” Journal of Robotics and Control (JRC), vol. 5, no. 5, pp. 1479-1491, 2024, https://doi.org/10.18196/jrc.v5i5.22690.
A. T. Azar, “Overview of type-2 fuzzy logic systems,” International Journal of Fuzzy System Applications (IJFSA), vol. 2, no. 4, pp. 1-28, 2012, https://doi.org/10.4018/ijfsa.2012100101.
A. Piegat and L. Dobryakova, “A decomposition approach to type 2 interval arithmetic,” International Journal of Applied Mathematics and Computer Science, vol. 30, no. 1, 2020, https://doi.org/10.34768/amcs-2020-0015.
Z. A. Al-Dabbagh, S. W. Shneen, and A. O. Hanfesh, “Fuzzy Logic-based PI Controller with PWM for Buck-Boost Converter,” Journal of Fuzzy Systems and Control, vol. 2, no. 3, pp. 147-159, 2024, https://doi.org/10.59247/jfsc.v2i3.239.
İ. Kaya and A. Turgut, “Design of variable control charts based on type-2 fuzzy sets with a real case study,” Soft Computing, vol. 25, pp. 613-633, 2021, https://doi.org/10.1007/s00500-020-05172-4.
M. Gülbay and C. Kahraman, “An alternative approach to fuzzy control charts: Direct fuzzy approach,” Information sciences, vol. 177, no. 6, pp. 1463-1480, 2007, https://doi.org/10.1016/j.ins.2006.08.013.
B. Amjad, S. Hussien, Z. J. Ghulam, and M. K. Rashed, “Quality Improvement of Petroleum Products Using Fuzzy Control Charts,” International Journal, vol. 5, no. 1, pp. 8-21, 2017, https://doi.org/10.18488/journal.63.2017.51.8.21.
N. Ben Khedher, A. Boudjemline, W. Aich, M. A. Zeddini, and J. E. Calderon-Madero, “Statistical quality control based on control charts and process efficiency index by the application of fuzzy approach (case study: Ha'il, Saudi Arabia),” Water Science & Technology, vol. 87, no. 12, pp. 3146-3163, 2023, https://doi.org/10.2166/wst.2023.181.
M. Ahmad and W. Cheng, “A novel approach of fuzzy control chart with fuzzy process capability indices using alpha cut triangular fuzzy number,” Mathematics, vol. 10, no. 19, p. 3572, 2022, https://doi.org/10.3390/math10193572.
N. Perrot, I. Ioannou, I. Allais, C. Curt, J. Hossenlopp, and G. Trystram, “Fuzzy concepts applied to food product quality control: A review,” Fuzzy sets and systems, vol. 157, no. 9, pp. 1145-1154, 2006, https://doi.org/10.1016/j.fss.2005.12.013.
N. P. Alakoc and A. Apaydin, “A Fuzzy Control Chart Approach for Attributes and Variables,” Engineering, Technology & Applied Science Research, vol. 8, no. 5, 2018, https://doi.org/10.48084/etasr.2192.
O. Engin, A. Çelik, and İ. Kaya, “A fuzzy approach to define sample size for attributes control chart in multistage processes: An application in engine valve manufacturing process,” Applied Soft Computing, vol. 8, no. 4, pp. 1654-1663, 2008, https://doi.org/10.1016/j.asoc.2008.01.005.
İ. Kaya, A. Karaşan, E. İlbahar, and B. Cebeci, “Analyzing attribute control charts for defectives based on intuitionistic fuzzy sets,” In Conference Proceedings of Science and Technology, vol. 3, no. 1, pp. 122-128, 2020, https://dergipark.org.tr/en/pub/cpost/issue/57935/778328.
M. Nasiri and S. A. Darestani, “A literature review investigation on quality control charts based on fuzzy logic,” International Journal of Productivity and Quality Management, vol. 18, no. 4, pp. 474-498, 2016, https://doi.org/10.1504/IJPQM.2016.077778.
I. Ertuğrul and M. Güneş, “The usage of fuzzy quality control charts to evaluate product quality and an application,” Analysis and Design of Intelligent Systems using Soft Computing Techniques, pp. 660-673, 2007, https://doi.org/10.1007/978-3-540-72432-2_67.
E. D. Thamer and I. H. Hussein, “Solving fuzzy attribute quality control charts with proposed ranking function,” Ibn AL-Haitham Journal For Pure and Applied Sciences, vol. 34, no. 2, pp. 33-41, 2021, https://doi.org/10.30526/34.2.2611.
N. Rubens, “The application of fuzzy logic to the construction of the ranking function of information retrieval systems,” arXiv preprint cs/0610039, 2006, https://doi.org/10.48550/arXiv.cs/0610039.
K. Ishikawa and J. H. Loftus. Introduction to quality control. vol. 98, p. 31. Tokyo: 3A Corporation. 1990. https://link.springer.com/book/9789401176903.
S. Şentürk, “Fuzzy regression control chart based on α-cut approximation,” International Journal of Computational Intelligence Systems, vol. 3, no. 1, pp. 123-140, 2010, https://doi.org/10.1080/18756891.2010.9727683.
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