红外辐射线检测技术的若干理论与应用研究
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摘要
光声和光热检测技术是一种重要的无损检测技术,由于其具有灵敏度高、频响宽、适应性强等优点,因此被广泛应用于物理、化学、生物、材料科学、航天航空及医疗诊断等各个领域。
本文是基于光声光热无损检测技术探索通过反演手段重建不均匀材料物理参数分布形态新方法的研究。先对两种光热辐射信号进行理论分析和实验验证,然后介绍一种由实测光声光热信号来实现剖面重构的新方法。
为了研究温度频谱信号与样品夹层的变化关系,在频域传热方程的基础上,建立了分层算法和拉普拉斯算法,并对两种算法进行比较,就两种方法的适用性和局限性作出理论分析。应用数值模拟的方法得到:(1)信号频谱的极值主要受夹层位置的影响,而几乎不受缺陷层宽度和光吸收强度的影响;(2)建立了一条特殊曲线(PMF曲线),通过该曲线,由实测信号的相位频谱可直接预测单夹层样品中缺陷的位置;(3)表面光热频谱信号的比值信号分析。
在以上工作的基础上,本文又进一步研究了红外辐射线频谱信号的理论分析与应用,主要内容有:(1)红外辐射信号的理论模型、数值模拟及与实测信号的比对;(2)红外辐射信号的演化及规律分析;(3)建立了特殊曲线(PMF曲线),通过该曲线,由实测信号的相位频谱可直接预测单夹层样品中缺陷的位置。结论不仅对于有关试验与反演算法的设计有着重要的指导作用,而且可直接应用于材料缺陷分析和剖面成像。
最后,研究了利用人工神经网络重构样品物理参数的新方法。主要内容有:(1)新型神经网络群的结构与算法设计;(2)网络的训练方法与技巧以及网络实用性能检验等;(3)对可能影响网络性能的诸多可能因素予以了详细分析,为网络的实际应用提供必要的经验。结果表明,所建人工神经网络具有下列优点:(1)网络具有极快的运算速度和批处理能力;(2)网络有良好的辨识与联想能力。经过适当训练的网络,不仅能够识别与训练集中函数性质相近的目标函数,也能识别性质不同的其它目标函数,特别是对于一些连续性和光滑性较差的目标函数,网络也能予以较好的重构;(3)网络具有较强的抗噪声能力,能够实施对实测信号的反演以解决实际问题。
Photoacoustic (PA) and photothermal (PT) techniques are important means of nondestructive detection. Because of their superior performance in high sensitive, broad detecting bandwidth and good adaptability, they have been used extensively in such fields as physics, chemistry, biology, materials science, medical diagnosis and aeronautic and spatial technologies.
Studied in this thesis is the exploration for founding new methods to reconstruct physical parameter distribution in materials based on PT technique. We start with the research in two kinds of PT signals by both theoretical analysis and experimental validation. Then, a new way of doing depth profiling reconstruction from measured PT data is presented, and more details are summarized in following.
Firstly, we make a characteristic analysis in evolution of photothermal temperature spectrum (PTS) responded with the variation of the interlayer included in a layered sample. In order to calculate PTS signals, two methods are presented to solve the heat conducting equation in frequency domain, and also theoretical analysis is given to discuss their practicality and limitations. The obtained results include: (1) the extreme values of the signal spectrum are mainly affected by the position of the defect, and without obvious variation while the width or absorption intensity of defect is changed; (2) a special curve is found from which one can predict the position of a defect by using detected signals; (3) more detailed analysis about the responding characteristic of PTS are displayed by signal ratio curves.
On the basis of the previous work, the theoretical analysis and application of photothermal radiometry infrared spectrum (PRIS) have been invested in detail. The main contents include: (1) to find the theoretical model of PRIS and make numerical simulations and compare the results with experimental data; (2) to give an analysis in the responding of the PRIS with the variation of the interlayer included in a layered sample; (3) to find a special curve which can be used to predict the position of the defects in a sample from practical signals. The obtained results are not only very helpful for constructing inverse algorithm but also can be used directly to analyze thedefect hided in a solid sample and to make depth profiling image of the sample.
In the last part of this thesis, we investigate the feasibility of using a new kind of artificial neural network (ANN) to reconstruct the depth profile of photo absorbing coefficients of samples from PTS or PRIS. The main contents include: (1) the structure design of a NN group which is able to treat inverse problems; (2) the training method of NN and its capability testing on prediction and association ability; (3) some factors that are possible to affect the inversing performance of the NN are discussed, which is useful to provides necessary experience for practical application of the NN. The numerical simulating results from large numbers of samples confirmed the advantages of the NN in following aspect: (1) quick calculating speed and its capacity of treating sample in batches; (2) good recognizing and associating ability, i.e., a well-trained NN can recognize not only the monotone and smooth functions but also those with discontinuous profiles; (3) high stability and validity in resisting noise disturbance. The introduced NN has potential application in the field of nondestructive detection of materials.
本文是基于光声光热无损检测技术探索通过反演手段重建不均匀材料物理参数分布形态新方法的研究。先对两种光热辐射信号进行理论分析和实验验证,然后介绍一种由实测光声光热信号来实现剖面重构的新方法。
为了研究温度频谱信号与样品夹层的变化关系,在频域传热方程的基础上,建立了分层算法和拉普拉斯算法,并对两种算法进行比较,就两种方法的适用性和局限性作出理论分析。应用数值模拟的方法得到:(1)信号频谱的极值主要受夹层位置的影响,而几乎不受缺陷层宽度和光吸收强度的影响;(2)建立了一条特殊曲线(PMF曲线),通过该曲线,由实测信号的相位频谱可直接预测单夹层样品中缺陷的位置;(3)表面光热频谱信号的比值信号分析。
在以上工作的基础上,本文又进一步研究了红外辐射线频谱信号的理论分析与应用,主要内容有:(1)红外辐射信号的理论模型、数值模拟及与实测信号的比对;(2)红外辐射信号的演化及规律分析;(3)建立了特殊曲线(PMF曲线),通过该曲线,由实测信号的相位频谱可直接预测单夹层样品中缺陷的位置。结论不仅对于有关试验与反演算法的设计有着重要的指导作用,而且可直接应用于材料缺陷分析和剖面成像。
最后,研究了利用人工神经网络重构样品物理参数的新方法。主要内容有:(1)新型神经网络群的结构与算法设计;(2)网络的训练方法与技巧以及网络实用性能检验等;(3)对可能影响网络性能的诸多可能因素予以了详细分析,为网络的实际应用提供必要的经验。结果表明,所建人工神经网络具有下列优点:(1)网络具有极快的运算速度和批处理能力;(2)网络有良好的辨识与联想能力。经过适当训练的网络,不仅能够识别与训练集中函数性质相近的目标函数,也能识别性质不同的其它目标函数,特别是对于一些连续性和光滑性较差的目标函数,网络也能予以较好的重构;(3)网络具有较强的抗噪声能力,能够实施对实测信号的反演以解决实际问题。
Photoacoustic (PA) and photothermal (PT) techniques are important means of nondestructive detection. Because of their superior performance in high sensitive, broad detecting bandwidth and good adaptability, they have been used extensively in such fields as physics, chemistry, biology, materials science, medical diagnosis and aeronautic and spatial technologies.
Studied in this thesis is the exploration for founding new methods to reconstruct physical parameter distribution in materials based on PT technique. We start with the research in two kinds of PT signals by both theoretical analysis and experimental validation. Then, a new way of doing depth profiling reconstruction from measured PT data is presented, and more details are summarized in following.
Firstly, we make a characteristic analysis in evolution of photothermal temperature spectrum (PTS) responded with the variation of the interlayer included in a layered sample. In order to calculate PTS signals, two methods are presented to solve the heat conducting equation in frequency domain, and also theoretical analysis is given to discuss their practicality and limitations. The obtained results include: (1) the extreme values of the signal spectrum are mainly affected by the position of the defect, and without obvious variation while the width or absorption intensity of defect is changed; (2) a special curve is found from which one can predict the position of a defect by using detected signals; (3) more detailed analysis about the responding characteristic of PTS are displayed by signal ratio curves.
On the basis of the previous work, the theoretical analysis and application of photothermal radiometry infrared spectrum (PRIS) have been invested in detail. The main contents include: (1) to find the theoretical model of PRIS and make numerical simulations and compare the results with experimental data; (2) to give an analysis in the responding of the PRIS with the variation of the interlayer included in a layered sample; (3) to find a special curve which can be used to predict the position of the defects in a sample from practical signals. The obtained results are not only very helpful for constructing inverse algorithm but also can be used directly to analyze thedefect hided in a solid sample and to make depth profiling image of the sample.
In the last part of this thesis, we investigate the feasibility of using a new kind of artificial neural network (ANN) to reconstruct the depth profile of photo absorbing coefficients of samples from PTS or PRIS. The main contents include: (1) the structure design of a NN group which is able to treat inverse problems; (2) the training method of NN and its capability testing on prediction and association ability; (3) some factors that are possible to affect the inversing performance of the NN are discussed, which is useful to provides necessary experience for practical application of the NN. The numerical simulating results from large numbers of samples confirmed the advantages of the NN in following aspect: (1) quick calculating speed and its capacity of treating sample in batches; (2) good recognizing and associating ability, i.e., a well-trained NN can recognize not only the monotone and smooth functions but also those with discontinuous profiles; (3) high stability and validity in resisting noise disturbance. The introduced NN has potential application in the field of nondestructive detection of materials.
引文
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