{"title":"Radar Evasion Control of Military Structures Using Graphene Oxide Coating RGO/NiFe2O4 and Polynomial Profile Monitoring","authors":"","doi":"10.47176/jame.41.4.1009","DOIUrl":null,"url":null,"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.","PeriodicalId":30992,"journal":{"name":"Journal of Advanced Materials in Engineering","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Advanced Materials in Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.47176/jame.41.4.1009","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
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.