Radar Evasion Control of Military Structures Using Graphene Oxide Coating RGO/NiFe2O4 and Polynomial Profile Monitoring

Document Type : Original Article

Authors

Malek Ashtar University of Technology, Industrial Engineering Faculty

Abstract

Tracking military structures and equipment is one of the parameters to create superiority in military battles. Camouflage has long been used to reduce the possibility of detection of military structures and equipment. Development of knowledge in the field of tracking and discovering military structures and equipment followed by the necessity of using the concealment in order to reduce vulnerability in war, has enhanced the importance of using new camouflage and radar evasion methods. The use of nanotechnology in the field of radar evasion of military facilities was developed by introducing graphene as a very strong absorber of electromagnetic waves. Graphene coating on the military installations causes the absorption of electromagnetic waves and as a result, these installations are not detected by the enemy's radar.  Referring to the fact that there is a functional relationship between the diameter of the graphene oxide used and the radar evasion of the equipment, an attempt has been made in this article for the first time to find a solution to control and monitor the radar evasion quality using graphene oxide (with the scientific name of RGO/NiFe2O4) in the microwave spectrum of 7GHZ by the profile approach and presentation of a regression relationship. This model can be used to monitor the quality of radar evasion products (cheaper and faster than existing methods). Finally, sensitivity analysis of the model showed that the ability to detect non-conformity in the manufactured products can be detected quickly (between 1 and 20 samples) with the change in the parameters of the regression model.

Keywords

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References

  1. Guarnieri M. The early history of radar [Historical]. IEEE Industrial Electronics Magazine 2010;4(3):36-42.
  2. Wu B, Tuncer HM, Naeem M, Yang B, Cole MT, Milne WI, et al. Experimental demonstration of a transparent graphene millimetre wave absorber with 28% fractional bandwidth at 140 GHz. Scientific reports 2014;4(1):4130.
  3. Wang C, Han X, Xu P, Zhang X, Du Y, Hu S, et al. The electromagnetic property of chemically reduced graphene oxide and its application as microwave absorbing material. Applied Physics Letters 2011; 98(7):072906.
  4. Beshkar F, Zinatloo-Ajabshir S, Salavati-Niasari M. Simple morphology-controlled fabrication of nickel chromite nanostructures via a novel route. Chemical Engineering Journal 2015;279:605-614.
  5. Pawar SP, Gandi M, Arief I, Krause B, Pötschke P, Bose S. Graphene derivatives doped with nickel ferrite nanoparticles as excellent microwave absorbers in soft nanocomposites. ChemistrySelect 2017;2(21):5984-99.
  6. Zinatloo-Ajabshir S, Heidari-Asil SA, Salavati-Niasari M. Simple and eco-friendly synthesis of recoverable zinc cobalt oxide-based ceramic nanostructure as high-performance photocatalyst for enhanced photocatalytic removal of organic contamination under solar light. Separation and Purification Technology 2021;267:118667.
  7. Green M, Chen X. Recent progress of nanomaterials for microwave absorption. Journal of Materiomics 2019;5(4):503-41.
  8. Zhang J, Li Z, Shao L, Zhu W. Dynamical absorption manipulation in a graphene-based optically transparent and flexible metasurface. Carbon 2021; 176:374-82.
  9. Ding P, Li M, Tian X, Li Y, Shao L, Xu K, et al. Graphene metasurface for broadband, wide-angle and polarization-insensitive carpet cloak. Optical Materials 2021;121:111578.
  10. Li J, Xu Z, Li T, Zhi D, Chen Y, Lu Q, et al. Multifunctional antimony tin oxide/reduced graphene oxide aerogels with wideband microwave absorption and low infrared emissivity. Composites Part B: Engineering 2022;231:109565.
  11. Wu K-H, Huang W-C, Wang J-C, Hung W-C. Infrared stealth and microwave absorption properties of reduced graphene oxide functionalized with Fe3O4. Materials Science and Engineering: B 2022;276: 115575.
  12. Marchesini S, Turner P, Paton KR, Reed BP, Brennan B, Koziol K, et al. Gas physisorption measurements as a quality control tool for the properties of graphene/graphite powders. Carbon 2020;167:585-95.
  13. Goldie SJ, Bush S, Cumming JA, Coleman KS. A statistical approach to raman analysis of graphene-related materials: implications for quality control. ACS Applied Nano Materials 2020;3(11):11229-39.
  14. Montgomery DC. Statistical quality control: Wiley New York; 2009.
  15. Hotteling H. Multivariate quality control, illustrated by the air testing of sample bombsights. Techniques of statistical analysis 1947:111-84.
  16. Mestek O, Pavlík J, Suchánek M. Multivariate control charts: control charts for calibration curves. Fresenius' journal of analytical chemistry 1994; 350(6):344-51.
  17. Stover FS, Brill RV. Statistical quality control applied to ion chromatography calibrations. Journal of Chromatography A 1998;804(1-2):37-43.
  18. Kang L, Albin SL. On-line monitoring when the process yields a linear profile. Journal of quality Technology 2000;32(4):418-26.
  19. Williams JD, Woodall WH, Birch JB, Sullivan JH. Distribution of Hotelling's T2 statistic based on the successive differences estimator. Journal of Quality Technology 2006;38(3):217-29.
  20. Nikoo M, Noorossana R. Phase II monitoring of nonlinear profile variance using wavelet. Quality and Reliability Engineering International 2013;29(7): 1081-9.
  21. Saghaei A, Maserrat N. Monitoring of the disease: new user profiles Case Study: Lung Disease. international journal of industrial engineering and production management 2008;22:54-63.
  22. Atashgar K, Amiri A, Nejad MK. Monitoring Allan variance nonlinear profile using artificial neural network approach (In Persian). International Journal of Quality Engineering and Technology 2015;5(2): 162-77.
  23. Deng Y, Hou B, Chen Y, Wang D, editors. Nonparametric nonlinear profile monitoring method for machine condition monitoring. Journal of Physics: Conference Series; 2022: IOP Publishing.
  24. Wang T, Wang Y, Zang Q. Outlier detection in non-parametric profile monitoring. Statistics 2022:1-18.
  25. Atashgar K. Advanced Statistical Process Control (Simple Linear Profile Monitoring) (In Persian): Malek Ashtar University of Technology; 2020.
  26. Noorossana R, Saghaei A, Amiri A. Statistical analysis of profile monitoring: Wiley Online Library; 2011.
  27. Kazemzadeh RB, Noorossana R, Amiri A. Phase I monitoring of polynomial profiles. Communications in Statistics—Theory and Methods 2008;37(10):1671-86.
  28. Williams JD, Woodall WH, Birch JB. Statistical monitoring of nonlinear product and process quality profiles. Quality and Reliability Engineering International 2007;23(8):925-41.
  29. Amiri A, Jensen WA, Kazemzadeh RB. A case study on monitoring polynomial profiles in the automotive industry. Quality and Reliability Engineering International 2010;26(5):509-20.
  30. Zong M, Huang Y, Ding X, Zhang N, Qu C, Wang Y. One-step hydrothermal synthesis and microwave electromagnetic properties of RGO/NiFe2O4 Ceramics International 2014;40(5):6821-8.
Journal of Advanced Materials in Engineering (Esteghlal)
Volume 41, Issue 4
March 2023
Pages 61-74

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