Two-Dimensional Arrays for the Quantitative Characterisation of Complex Defects

用于复杂缺陷定量表征的二维阵列

基本信息

  • 批准号:
    EP/F004680/1
  • 负责人:
  • 金额:
    $ 36.26万
  • 依托单位:
  • 依托单位国家:
    英国
  • 项目类别:
    Research Grant
  • 财政年份:
    2008
  • 资助国家:
    英国
  • 起止时间:
    2008 至 无数据
  • 项目状态:
    已结题

项目摘要

Recent years have seen a rapid increase in the interest in arrays for NDE. Their advantages include increased flexibility, as one array probe can undertake many inspections, increased speed of imaging and increased coverage from a given location. However, current NDE systems utilise mainly 1-Dimensional (1D) arrays and so are limited to obtaining a 2-Dimensional (2D) image. 2D arrays offer the potential to image in all three dimensions. This has clear benefits as real defects and engineering structures are three-dimensional / for example defects such as cracking and inclusions in welds are of arbitrary shape and can occur in arbitrary orientations. 2D arrays are able to 'view' a given defect form a range of angles leading to the possibility of obtaining characterisation detail far beyond what is currently achievable. However there are a number of obstacles currently stifling the application of 2D arrays to NDE. Firstly, how should 2D arrays be designed with a low enough number of elements to be practically viable? Secondly, how should the vast amount of data potentially obtainable be minimised and processed? Thirdly, given these practical limitations, what imaging and characterisation performance can be achieved? This project will address these issues by developing a modelling suite to optimise array design, investigating a range of data management and signal processing techniques to make the most efficient use of the data, incorporating recently developed piezoelectric and passive materials into the array design and optimising 2D array manufacture for NDE. This proposal is being submitted within the UK Research Centre in NDE to the targeted research programme, the funding for which is earmarked by EPSRC for industrially driven research.
近年来,人们对无损检测阵列的兴趣迅速增加。它们的优势包括提高灵活性,因为一个阵列探头可以进行多次检查,提高成像速度,并从给定位置扩大覆盖范围。然而,目前的无损检测系统主要使用一维(1D)阵列,因此仅限于获得二维(2D)图像。2D阵列提供了在所有三个维度上成像的潜力。这有明显的好处,因为真正的缺陷和工程结构是三维的,例如,焊缝中的裂纹和夹杂物等缺陷是任意形状的,可以在任意方向上发生。2D阵列能够从一定范围的角度“观察”给定的缺陷,从而有可能获得远远超出目前所能实现的表征细节。然而,目前有许多障碍阻碍了二维阵列在无损检测中的应用。首先,2D阵列应该如何设计为具有足够少的元素数量才能实际可行?其次,应该如何最小化和处理可能获得的海量数据?第三,考虑到这些实际限制,成像和表征性能可以达到什么水平?该项目将通过开发优化阵列设计的建模套件来解决这些问题,研究一系列数据管理和信号处理技术以最有效地利用数据,将最新开发的压电和无源材料纳入阵列设计,并优化用于无损检测的2D阵列制造。这项建议正在提交给位于NDE的英国研究中心的目标研究计划,其资金由EPSRC指定用于工业驱动的研究。

项目成果

期刊论文数量(10)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Conformally mapped 2D ultrasonic array structure for NDT imaging application
用于无损检测成像应用的共形映射二维超声阵列结构
  • DOI:
    10.1109/ultsym.2010.5935908
  • 发表时间:
    2010
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Ramadas S
  • 通讯作者:
    Ramadas S
A model for early life availability growth of a system of systems
系统系统早期可用性增长的模型
  • DOI:
  • 发表时间:
    2013
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Walls L
  • 通讯作者:
    Walls L
Availability growth and state-of-knowledge uncertainty simulation for offshore wind farms
海上风电场的可用性增长和知识状态不确定性模拟
  • DOI:
  • 发表时间:
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Athena Zitrou (Author)
  • 通讯作者:
    Athena Zitrou (Author)
Tapered transmission line technique based graded matching layers for thickness mode piezoelectric transducers
基于渐变匹配层的厚度模式压电换能器的锥形传输线技术
  • DOI:
    10.1109/ultsym.2009.5441463
  • 发表时间:
    2009
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Ramadas S
  • 通讯作者:
    Ramadas S
A 2D Ultrasonic array design incorporating hexagonal-shaped elements and triangular-cut piezocomposite substructure for NDE applications
二维超声波阵列设计,采用六边形元件和三角形切割压电复合材料子结构,适用于 NDE 应用
  • DOI:
    10.1109/ultsym.2009.5441447
  • 发表时间:
    2009
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Dziewierz J
  • 通讯作者:
    Dziewierz J
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Anthony Gachagan其他文献

Development of a phased array ultrasound roller probe for inspection of wire + arc additive manufactured components
用于检测电弧增材制造部件的相控阵超声滚轮探头的开发
  • DOI:
    10.1016/j.jmapro.2022.06.045
  • 发表时间:
    2022-08-01
  • 期刊:
  • 影响因子:
    6.800
  • 作者:
    Randika K.W. Vithanage;Ehsan Mohseni;David Lines;Charalampos Loukas;Euan Foster;Charles N. MacLeod;S. Gareth Pierce;Anthony Gachagan;Jialuo Ding;Stewart Williams
  • 通讯作者:
    Stewart Williams
Application of Golay-based total focusing method using a high-frequency, lead-free, flexible ultrasonic array for inspection of thick non-planar industrial components
基于高频率、无铅、柔性超声阵列的戈莱总聚焦方法在厚非平面工业部件检测中的应用
  • DOI:
    10.1016/j.ndteint.2024.103282
  • 发表时间:
    2025-03-01
  • 期刊:
  • 影响因子:
    4.500
  • 作者:
    Elmergue Germano;Morteza Tabatabaeipour;Ehsan Mohseni;David Lines;Charles N. MacLeod;Kwok-Ho Lam;David Hughes;Heather Trodden;Anthony Gachagan
  • 通讯作者:
    Anthony Gachagan
Automated sizing and classification of defects in CANDU pressure tubes
  • DOI:
    10.1016/j.nucengdes.2017.09.029
  • 发表时间:
    2017-12-15
  • 期刊:
  • 影响因子:
  • 作者:
    Timothy Lardner;Graeme West;Gordon Dobie;Anthony Gachagan
  • 通讯作者:
    Anthony Gachagan
Online evolution of a phased array for ultrasonic imaging by a novel adaptive data acquisition method
通过新型自适应数据采集方法在线演化超声成像相控阵
  • DOI:
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    4.6
  • 作者:
    Peter Lukacs;T. Stratoudaki;Geo Davis;Anthony Gachagan
  • 通讯作者:
    Anthony Gachagan
A theoretical investigation of chirp insonification of ultrasound contrast agents
  • DOI:
    10.1016/j.ultras.2011.02.009
  • 发表时间:
    2011-08-01
  • 期刊:
  • 影响因子:
  • 作者:
    Euan Barlow;Anthony J. Mulholland;Anthony Gachagan;Alison Nordon
  • 通讯作者:
    Alison Nordon

Anthony Gachagan的其他文献

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{{ truncateString('Anthony Gachagan', 18)}}的其他基金

NEW METHODS FOR ULTRASONIC NDE OF DIFFICULT MATERIALS
难加工材料超声波无损检测新方法
  • 批准号:
    EP/I019731/1
  • 财政年份:
    2011
  • 资助金额:
    $ 36.26万
  • 项目类别:
    Research Grant
Generation, Detection & Analysis of Optimally Coded Ultrasonic Waveforms
生成、检测
  • 批准号:
    EP/F017421/1
  • 财政年份:
    2008
  • 资助金额:
    $ 36.26万
  • 项目类别:
    Research Grant
PROCESS INTENSIFICATION USING HIGH INTENSITY FOCUSSED ULTRASOUND TECHNIQUES
使用高强度聚焦超声技术强化工艺
  • 批准号:
    EP/F016522/1
  • 财政年份:
    2008
  • 资助金额:
    $ 36.26万
  • 项目类别:
    Research Grant

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Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis
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