Defensive Future Studies

Defensive Future Studies

Improving the quality control of technical items in the defense industry with the technique of image processing and fuzzy transformation using the GLR control chart

Document Type : Original Article

Authors
1 Assistant Professor of Operations Research, IRI Military Command and Staff University, Tehran, Iran.
2 Associate Professor of Electrical Engineering, IRI Military Command and Staff University. Tehran. Iran
3 Researcher in Science and Technology Studies, IRI Military Command and Staff University, Tehran, Iran
Abstract
Objective: The main goal is to propose a model for quality control in the defense industry, utilizing image processing and fuzzy transform. Emphasis is on selecting the optimal generalized fuzzy transform section for image compression to enhance the performance of defense and combat weapons.
Methodology: The methodology employs modern image processing and fuzzy transform techniques for statistical quality control. High-volume data analysis occurs in production lines, managing processes for defense and combat weapon products like glass and metals. MATLAB is the implementation platform, emphasizing the optimal selection of the generalized fuzzy transform section for image compression and processing.
Results: MATLAB validation confirms the success of our model in quality control for defense systems. Comparative studies show the triangular fuzzy section model excels, especially in defect detection post-illumination changes, surpassing Kusha et al. in most cases.
Conclusion: In conclusion, our study emphasizes the vital role of image processing and fuzzy transform techniques in defense. The developed model successfully achieves quality control goals, optimizes processes, and enhances defense and combat weapon quality. This reflects a growing industry trend, with an increasing adoption of these methods to meet goals and address challenges.
Keywords

  • Arunpandian, S., & Dhenakaran, S. S. (2022). An effective image compression technique based on burrows wheeler transform with set partitioning in hierarchical trees. Concurrency and Computation: Practice and Experience, 34(5), e
  • Bhalla, K., Koundal, D., Sharma, B., Hu, Y. C., & Zaguia, A. (2022). A fuzzy convolutional neural network for enhancing multi-focus image fusion. Journal of Visual Communication and Image Representation, 84, 103485.
  • Colosimo, B. M. (2018). Modeling and monitoring methods for spatial and image data. Quality Engineering, 30 (1), 94-111.
  • Colosimo, B. M., & Pacella, M. (2010). A comparison study of control charts for statistical monitoring of functional data. International Journal of Production Research, 48(6), 1575-1601.
  • Colosimo, B. M., Cicorella, P., Pacella, M., & Blaco, M. (2014). From profile to surface monitoring: SPC for cylindrical surfaces via Gaussian processes. Journal of Quality Technology, 46(2), 95-113.
  • Colosimo, B. M., Semeraro, Q., & Pacella, M. (2008). Statistical process control for geometric specifications: on the monitoring of roundness profiles. Journal of quality technology, 40(1), 1-18.
  • Di Martino, F., Loia, V., & Sessa, S. (2011). Fuzzy transforms method in prediction data analysis. Fuzzy Sets and Systems, 180 (1), 146-163.
  • Duchesne, C., Liu, J. J., & MacGregor, J. F. (2012). Multivariate image analysis in the process industries: A review. Chemometrics and Intelligent Laboratory Systems, 117, 116-128.
  • Garg, G., & Kumar, R. (2022). Analysis of image types, compression techniques and performance assessment metrics: A review. Journal of Information and Optimization Sciences, 43(3), 429-436.
  • Hanmandlu, M., & Jha, D. (2006). An optimal fuzzy system for color image enhancement. IEEE Transactions on image processing, 15(10), 2956-2966.
  • Karabassis, E., & Spetsakis, M. E. (1995). An analysis of image interpolation, differentiation, and reduction using local polynomial fits. Graphical models and image processing, 57(3), 183-196.
  • Khastan, A., Perfilieva, I., & Alijani, Z. (2016). A new fuzzy approximation method to Cauchy problems by fuzzy transform. Fuzzy Sets and Systems, 288, 75-95.
  • Koosha, M., Noorossana, R., & Megahed, F. (2017). Statistical process monitoring via image data using wavelets. Quality and Reliability Engineering International, 33(8), 2059-2073.
  • Liu, Z., Blasch, E., & John, V. (2017). Statistical comparison of image fusion algorithms: Recommendations. Information Fusion, 36, 251-260.
  • Megahed, F. M., Woodall, W. H., & Camelio, J. A. (2011). A review and perspective on control charting with image data. Journal of quality technology, 43(2), 83-98.
  • Mehrafrooz, Z., & Noorossana, R. (2011). An integrated model based on statistical process control and maintenance. Computers & Industrial Engineering, 61(4), 1245-1255.
  • Močkoř, J., & Hurtík, P. (2021). Approximations of fuzzy soft sets by fuzzy soft relations with image processing application. Soft Computing, 25(10), 6915-6925.
  • N‌a‌j‌i‌b‌i, S. S., A‌m‌i‌r‌i, A. H., & A‌m‌i‌r‌k‌h‌a‌n‌i, F. (2020). A‌N I‌N‌T‌E‌G‌R‌A‌T‌E‌D M‌O‌D‌E‌L O‌F S‌T‌A‌T‌I‌S‌T‌I‌C‌A‌L P‌R‌O‌C‌E‌S‌S C‌O‌N‌T‌R‌O‌L A‌N‌D M‌A‌I‌N‌T‌E‌N‌A‌N‌C‌E B‌A‌S‌E‌D O‌N D‌E‌L‌A‌Y‌E‌D M‌O‌N‌I‌T‌O‌R‌I‌N‌G I‌N T‌W‌O-S‌T‌A‌G‌E P‌R‌O‌C‌E‌S‌S‌E‌S. Sharif Journal of Industrial Engineering & Management, 35 (2.2), 81-92.
  • Nirmalraj, S., & Nagarajan, G. (2021). Biomedical image compression using fuzzy transform and deterministic binary compressive sensing matrix. Journal of Ambient Intelligence and Humanized Computing, 12, 5733-5741.
  • Paternain, D., Fernández, J., Bustince, H., Mesiar, R., & Beliakov, G. (2015). Construction of image reduction operators using averaging aggregation functions. Fuzzy Sets and Systems, 261, 87-111.
  • Perfilieva, I., & Adamczyk, D. (2022, July). Selection of Keypoints in 2D Images Using F-Transform. In International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (pp. 418-430). Cham: Springer International Publishing.
  • Perfilieva, I., Holčapek, M., & Kreinovich, V. (2016). A new reconstruction from the F-transform components. Fuzzy Sets and Systems, 288, 3-25.
  • Reis, M. S., & Gins, G. (2017). Industrial process monitoring in the big data/industry 4.0 era: From detection, to diagnosis, to prognosis. Processes, 5(3), 35.
  • Sessa, S., Di Martino, F., & Perfilieva, I. G. (2013). Fuzzy functions, relations, and fuzzy transforms 2013. Advances in Fuzzy Systems, 2013, 6-6.
  • Yin, H., Zhang, G., Zhu, H., Deng, Y., & He, F. (2015). An integrated model of statistical process control and maintenance based on the delayed monitoring. Reliability Engineering & System Safety, 133, 323-333.