Evaluating the Quality of Manufactured Products by Providing an Approach based on the ANFIS Neural-Fuzzy Network (Case Study: Khazar Plastic Manufacturing and Industrial Company)

Document Type : Original Article

Authors

1 PhD student, Department of Management, Faculty of Management and Economics, University of Guilan, Guilan, Iran.

2 Associate Professor, Department of Civil Engineering, Technical and Engineering Faculty, University of Guilan, Guilan, Iran.

3 Associate Professor, Department of Management, Faculty of Management and Economics, University of Guilan, Guilan, Iran.

Abstract

Introduction and Purpose: The current need for expanding accurate and rapid quality assurance to provide high-quality and safe manufactured products is essential. Quality focuses on internal issues that control and improve internal processes, aiming to enhance performance for customer satisfaction and competitiveness. Quality in industry, government, and society emphasizes the continuous improvement of products and processes. The quality performance evaluation system heavily relies on identifying and selecting critical success factors and indicators within the quality management framework. However, the manufacturing industry faces problems such as low production efficiency, low accuracy, and lack of innovation in products.
Methods: To address these issues, this study proposes introducing an artificial intelligence method for manufacturing companies to solve these problems and improve product quality and production efficiency. An adaptive neuro-fuzzy inference system (ANFIS) is presented to evaluate the accuracy of the results and compare its efficiency. The proposed method contrasts with hard calculations and aims to save time and money. As data volumes in industries increase, leading to new concepts like big data analytics, the study discusses the limitations and advantages of AI-based solutions. The focus is on stimulating creative solutions and new directions in manufacturing, commercial, and service industries to improve process efficiency, enhance the value of solutions, and design new products to find new markets. International roadmaps focused on innovation and research consistently highlight AI as a fundamental driver of future technology.
Traditional quality management methods face challenges in managing high-dimensional and non-linear production data. To address these challenges, this research develops an AI-based process to improve product quality and production efficiency. An adaptive neuro-fuzzy inference model, combining the advantages of neural networks and fuzzy inference, is proposed to evaluate and extract the quality level of manufactured products and infer the relationships between production parameters and process quality in a production system.
Findings: To train the proposed model, data from the quality process of a piece from Khazar Plastic Industrial Company's production line were used. The study included 550 data points related to the quality process, emphasizing influential variables such as "appearance standard, external diameter of the large gear, gear thickness, length of the metal shaft, height of the metal shaft, and external diameter of the metal shaft." These variables were considered input variables, and the final quality was the output variable. The accuracy of the results and the effectiveness of the proposed ANFIS model were evaluated using statistical indices, including the correlation coefficient and root-mean-square error.
Conclusion: The results indicate that the data used to evaluate the quality of the production part in the proposed adaptive neuro-fuzzy method show a good match between the model output quality and actual values, with a correlation coefficient of 0.95 and a mean square error of 0.42869.

Keywords

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References

  1. Al-Marakeby, A., Aly, A., & Salem, F. A. (2013). Fast quality inspection of food products using computer vision, J. Adv. Res. Comput. Commun. Eng. 2, 4168–4171.
  2. Behfarnia, K., & Khademi, F. (2017). A comprehensive study on the concrete compressive strength estimation using artificial neural network and adaptive neuro-fuzzy inference system. J. Optim. Civil Eng. 7(1), 71-80.
  3. Da, F. (2000). Decentralized sliding mode adaptive controller design based on fuzzy neural networks for interconnected uncertain nonlinear systems. IEEE Transactions on Neural Networks, 11(6), 1471-1480.
  4. Eslmpanah, M. (2023). Identifying Strategies and Applicable Policies to Improve the Standardization and Quality Management System to Achieve the Vision of the Islamic Republic of Iran in the Horizon of 1404. The Journal of Industrial Management Perspective, 13(50), 187-210. DOI: 10.52547/JIMP. 13.2.187. (In Persian)
  5. Field, J.M., Sinha, K.K. (2005) Applying process knowledge for yield variation reduction: A longitudinal field study. Decision.
  6. Hopp W.J., & Spearman, M.L. (2011). Factory Physics, 3rd ed. (Waveland Press, Long Grove, IL).
  7. Huang, X. (2022). Application of artificial intelligence APP in quality evaluation of primary school science education. Educational Studies, 1-21.
  8. Ittner, C. D. (1994). An examination of the indirect productivity gains from quality improvement. Production and Operations Management, 3(3), 153-170.
  9. JE, D. (2001). Artificial neural networks: opening the black box. Cancer, 91, 1615-1635.
  10. Karamouz, S. S., Ahmadi Kahnali, R., & Ghafurnia, M. (2019). Performance Measurement of Supply Chain Quality Management by Combination Balanced Scorecard and System Dynamics. Journal of Industrial Management Perspective, 9(3, Autumn 2019), 165-193. (In Persian)
  11. Karimi, A., Darabi, R., Poor, F. M., & Moghadam, H. (2022). Predicting Information Quality Ranking with Factor Analysis and Artificial Intelligence Approach.
  12. Lin, C. T., & Lee, C. G. (1996). Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems, 205, Prentice hall PTR Upper Saddle River NJ,
  13. Long, G. J., Lin, B. H., Cai, H. X., & Nong, G. Z. (2020). Developing an artificial intelligence (AI) management system to improve product quality and production efficiency in furniture manufacture. Procedia Computer Science, 166, 486-490.
  14. Makridakis, S. (2017). The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms. Futures, 90, 46-60.
  15. Malik, V., & Singh, S. (2020). Artificial intelligent environments: risk management and quality assurance implementation. Journal of Discrete Mathematical Sciences and Cryptography, 23(1), 187-195.
  16. Nelles, O., & Nelles, O. (2020). Nonlinear dynamic system identification (pp. 831-891). Springer International Publishing.
  17. Pedrycz, W. (1993). Fuzzy neural networks and neurocomputations. Fuzzy Sets and Systems, 56(1), 1-28.
  18. Prentice, C., Dominique Lopes, S., & Wang, X. (2020). The impact of artificial intelligence and employee service quality on customer satisfaction and loyalty. Journal of Hospitality Marketing & Management, 29(7), 739-756.
  19. Psarakis, S. (2011). The use of neural networks in statistical process control charts. Quality and Reliability Engineering International, 27(5), 641-650.
  20. Rajawat, A. S., Rawat, R., Barhanpurkar, K., Shaw, R. N., & Ghosh, A. (2021). Robotic process automation with increasing productivity and improving product quality using artificial intelligence and machine learning. In Artificial Intelligence for Future Generation Robotics (pp. 1-13). Elsevier.
  21. Schmenner, R. W., & Swink, M. L. (1998). On theory in operations management. Journal of operations management, 17(1), 97-113.
  22. Shahin, A., Janatyan, N., & Khodaparastan, M. (2021). Designing an Integrated Method for Increasing Quality of Product through Its Lifetime by Taguchi Design of Experiments and PAF Model (The Case of Entekhab Industrial Group). Journal of Industrial Management Perspective, 11(4), 37-57. (In Persian)
  23. Shewhart, W. A. (1926). Quality control charts. The Bell System Technical Journal, 5(4), 593-603.
  24. Somasundaram, M. J. K. M. S., Junaid, K. M., & Mangadu, S. (2020). Artificial intelligence (AI) enabled intelligent quality management system (IQMS) for personalized learning path. Procedia Computer Science, 172, 438-442.
  25. Taguchi, G. (1986). Introduction to Quality Engineering: Designing Quality into Products and Processes (Asian Productivity Organization, Tokyo).
  26. Weese, M., Martinez, W., Megahed, F. M., & Jones-Farmer, L. A. (2016). Statistical learning methods applied to process monitoring: An overview and perspective. Journal of Quality Technology, 48(1), 4-24.
  27. Woodall, W. H., & Montgomery, D. C. (2014). Some current directions in the theory and application of statistical process monitoring. Journal of Quality Technology, 46(1), 78-94.
  28. R. Huang (2019). Review and Prospect of New Generation Artificial Intelligence Research [J/OL]. Journal of Xinjiang Normal University (Philosophy and Social Sciences Edition), https://doi.org/10.14100/j. cnki. 65-1039/g4.20190312.001.
  29. H. Liang. Design of Intelligent Enterprise Management System Based on Big Data [J/OL]. Modern Electronic Technology,2019 (06): 158-161[
  30. Yadollahi, M. M., Benli, A., & Demirboga, R. (2017). Application of adaptive neuro-fuzzy technique and regression models to predict the compressive strength of geopolymer composites. Neural Computing and Applications, 28, 1453-1461.
  31. Zadeh, L.A. (1965). Fuzzy Sets, Information and control. California, 8(3), 338-353.
  32. Zantek, P. F., Wright, G. P., & Plante, R. D. (2002). Process and product improvement in manufacturing systems with correlated stages. Management Science, 48(5), 591-606.
  33. Zare Ahmedabadi, H., Habib, Safari Derbarzi, Mirghfouri, Seyyed Habibullah, Jafari Nadushan, & Masoud. (2021). Evaluation of the service quality of pharmaceutical distribution companies with the combined approach of Kano, Seroqual and artificial intelligence network. Scientific Quarterly of Standard and Quality Management, 10(4), 110-131.‎ (In Persian)
  34. Zhang, B., Huang, W., Li, J., Zhao, C., Fan, S., Wu, J. and Liu, C. (2014) Principles, Developments, and Applications of Computer Vision for External Quality Inspection of Fruits and Vegetables: A Review. Food Research International, 62, 326-343. https://doi.org/10.1016/j.foodres.2014.03.012.
  35. Zhang, S., Omar, A. H., Hashim, A. S., Alam, T., Khalifa, H. A. E. W., & Elkotb, M. A. (2023). Enhancing waste management and prediction of water quality in the sustainable urban environment using optimized algorithm of least square support vector machine and deep learning techniques. Urban Climate, 49, 101487.
  36. Zhang, Z., Zhang, K., & Khelifi, A. (2018). Multivariate time series analysis in climate and environmental research (p. 287). Cham: Springer International Publishing.

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