{"title":"基于自适应Var-MRLA稀疏方法的频域超声成像效率优化","authors":"Xiao Li, Shijie Jin, Chengjun Di, Zhongbing Luo","doi":"10.1016/j.measurement.2025.119181","DOIUrl":null,"url":null,"abstract":"<div><div>The time-domain total focusing method (TFM) in ultrasonic testing faces the issue of computational inefficiency, primarily due to the large-scale full matrix capture (FMC) datasets and the complex delay-and-sum (DAS) algorithm. In this paper, a new design scheme based on the frequency-domain TFM for sparse arrays is proposed by linking element selection with the FMC datasets to maintain stable imaging performance and improve imaging efficiency. A subset of transmitters containing critical information is dynamically selected through variance analysis, and the array arrangements are optimized according to the minimum redundancy principle. Experimental results demonstrate strong adaptability in detecting side-drilled holes (SDHs) with different spacings and positions in aluminum alloy specimens, and the beam directivity is maintained at a level comparable to the original array. The imaging time is reduced by 45.6 % at least, and the measurement errors of equivalent diameters are within 0.2 mm. Finally, simulation and experimental tests are conducted under different conditions, including steel material, planar defects, and double-layer media. The defect indications and quantitative results further confirm the great imaging performance of the proposed method compared to the original array and conventional sparse arrays.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"258 ","pages":"Article 119181"},"PeriodicalIF":5.6000,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Efficiency optimization of frequency-domain ultrasonic imaging by adaptive Var-MRLA sparse method\",\"authors\":\"Xiao Li, Shijie Jin, Chengjun Di, Zhongbing Luo\",\"doi\":\"10.1016/j.measurement.2025.119181\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The time-domain total focusing method (TFM) in ultrasonic testing faces the issue of computational inefficiency, primarily due to the large-scale full matrix capture (FMC) datasets and the complex delay-and-sum (DAS) algorithm. In this paper, a new design scheme based on the frequency-domain TFM for sparse arrays is proposed by linking element selection with the FMC datasets to maintain stable imaging performance and improve imaging efficiency. A subset of transmitters containing critical information is dynamically selected through variance analysis, and the array arrangements are optimized according to the minimum redundancy principle. Experimental results demonstrate strong adaptability in detecting side-drilled holes (SDHs) with different spacings and positions in aluminum alloy specimens, and the beam directivity is maintained at a level comparable to the original array. The imaging time is reduced by 45.6 % at least, and the measurement errors of equivalent diameters are within 0.2 mm. Finally, simulation and experimental tests are conducted under different conditions, including steel material, planar defects, and double-layer media. The defect indications and quantitative results further confirm the great imaging performance of the proposed method compared to the original array and conventional sparse arrays.</div></div>\",\"PeriodicalId\":18349,\"journal\":{\"name\":\"Measurement\",\"volume\":\"258 \",\"pages\":\"Article 119181\"},\"PeriodicalIF\":5.6000,\"publicationDate\":\"2025-10-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Measurement\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0263224125025400\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Measurement","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0263224125025400","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
Efficiency optimization of frequency-domain ultrasonic imaging by adaptive Var-MRLA sparse method
The time-domain total focusing method (TFM) in ultrasonic testing faces the issue of computational inefficiency, primarily due to the large-scale full matrix capture (FMC) datasets and the complex delay-and-sum (DAS) algorithm. In this paper, a new design scheme based on the frequency-domain TFM for sparse arrays is proposed by linking element selection with the FMC datasets to maintain stable imaging performance and improve imaging efficiency. A subset of transmitters containing critical information is dynamically selected through variance analysis, and the array arrangements are optimized according to the minimum redundancy principle. Experimental results demonstrate strong adaptability in detecting side-drilled holes (SDHs) with different spacings and positions in aluminum alloy specimens, and the beam directivity is maintained at a level comparable to the original array. The imaging time is reduced by 45.6 % at least, and the measurement errors of equivalent diameters are within 0.2 mm. Finally, simulation and experimental tests are conducted under different conditions, including steel material, planar defects, and double-layer media. The defect indications and quantitative results further confirm the great imaging performance of the proposed method compared to the original array and conventional sparse arrays.
期刊介绍:
Contributions are invited on novel achievements in all fields of measurement and instrumentation science and technology. Authors are encouraged to submit novel material, whose ultimate goal is an advancement in the state of the art of: measurement and metrology fundamentals, sensors, measurement instruments, measurement and estimation techniques, measurement data processing and fusion algorithms, evaluation procedures and methodologies for plants and industrial processes, performance analysis of systems, processes and algorithms, mathematical models for measurement-oriented purposes, distributed measurement systems in a connected world.