{"title":"用于纤维增强聚合物表层下缺陷检测的四次调频热波成像中的特征识别技术","authors":"Naga Prasanthi Yerneni, V. S. Ghali, G. T. Vesala","doi":"10.1134/S1061830923600788","DOIUrl":null,"url":null,"abstract":"<p>Efficient processing and stimulation mechanisms facilitating subsurface feature analysis are of prime concern in composite inspection. Being capable of presenting depth resolution and depth scanning with frequency sweep at low powers makes quadratic chirp an attractive stimulation mechanism and chirp Z-phased post-processing mechanism. This paper explores this mechanism with existing contemporary approaches and presents its novel feature exhibition enhancement capability through an inspection carried over a carbon fiber reinforced polymer (CFRP) composite specimen with embedded flat bottom holes. The defect detection performance is evaluated using the defect signal-to-noise ratio (SNR) for all the feature extraction algorithms. The SNR, characteristic parameter versus defect size and depth parameters reveal that the time domain PC and frequency domain CZT phase exhibit significantly high SNR and good correlation with the defect depth.</p>","PeriodicalId":764,"journal":{"name":"Russian Journal of Nondestructive Testing","volume":null,"pages":null},"PeriodicalIF":0.9000,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Feature Recognition in Quadratic Frequency Modulated Thermal Wave Imaging for Subsurface Defect Detection in Fiber-Reinforced Polymers\",\"authors\":\"Naga Prasanthi Yerneni, V. S. Ghali, G. T. Vesala\",\"doi\":\"10.1134/S1061830923600788\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Efficient processing and stimulation mechanisms facilitating subsurface feature analysis are of prime concern in composite inspection. Being capable of presenting depth resolution and depth scanning with frequency sweep at low powers makes quadratic chirp an attractive stimulation mechanism and chirp Z-phased post-processing mechanism. This paper explores this mechanism with existing contemporary approaches and presents its novel feature exhibition enhancement capability through an inspection carried over a carbon fiber reinforced polymer (CFRP) composite specimen with embedded flat bottom holes. The defect detection performance is evaluated using the defect signal-to-noise ratio (SNR) for all the feature extraction algorithms. The SNR, characteristic parameter versus defect size and depth parameters reveal that the time domain PC and frequency domain CZT phase exhibit significantly high SNR and good correlation with the defect depth.</p>\",\"PeriodicalId\":764,\"journal\":{\"name\":\"Russian Journal of Nondestructive Testing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.9000,\"publicationDate\":\"2024-01-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Russian Journal of Nondestructive Testing\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://link.springer.com/article/10.1134/S1061830923600788\",\"RegionNum\":4,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"MATERIALS SCIENCE, CHARACTERIZATION & TESTING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Russian Journal of Nondestructive Testing","FirstCategoryId":"88","ListUrlMain":"https://link.springer.com/article/10.1134/S1061830923600788","RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"MATERIALS SCIENCE, CHARACTERIZATION & TESTING","Score":null,"Total":0}
引用次数: 0
摘要
摘要 高效的处理和激励机制有助于分析地下特征,是复合材料检测的首要问题。二次啁啾能够在低功率下通过频率扫描实现深度分辨率和深度扫描,因此是一种极具吸引力的激励机制和啁啾 Z 相位后处理机制。本文通过对带有嵌入式平底孔的碳纤维增强聚合物(CFRP)复合材料试样进行检测,探讨了该机制与现有现代方法的不同之处,并展示了其新颖的特征展示增强能力。所有特征提取算法都使用缺陷信噪比(SNR)来评估缺陷检测性能。信噪比、特征参数与缺陷尺寸和深度参数的关系表明,时域 PC 和频域 CZT 相位的信噪比明显较高,并且与缺陷深度具有良好的相关性。
Feature Recognition in Quadratic Frequency Modulated Thermal Wave Imaging for Subsurface Defect Detection in Fiber-Reinforced Polymers
Efficient processing and stimulation mechanisms facilitating subsurface feature analysis are of prime concern in composite inspection. Being capable of presenting depth resolution and depth scanning with frequency sweep at low powers makes quadratic chirp an attractive stimulation mechanism and chirp Z-phased post-processing mechanism. This paper explores this mechanism with existing contemporary approaches and presents its novel feature exhibition enhancement capability through an inspection carried over a carbon fiber reinforced polymer (CFRP) composite specimen with embedded flat bottom holes. The defect detection performance is evaluated using the defect signal-to-noise ratio (SNR) for all the feature extraction algorithms. The SNR, characteristic parameter versus defect size and depth parameters reveal that the time domain PC and frequency domain CZT phase exhibit significantly high SNR and good correlation with the defect depth.
期刊介绍:
Russian Journal of Nondestructive Testing, a translation of Defectoskopiya, is a publication of the Russian Academy of Sciences. This publication offers current Russian research on the theory and technology of nondestructive testing of materials and components. It describes laboratory and industrial investigations of devices and instrumentation and provides reviews of new equipment developed for series manufacture. Articles cover all physical methods of nondestructive testing, including magnetic and electrical; ultrasonic; X-ray and Y-ray; capillary; liquid (color luminescence), and radio (for materials of low conductivity).