CN113884014B - Terahertz metal substrate multi-coating high-resolution thickness measurement method and device - Google Patents
Terahertz metal substrate multi-coating high-resolution thickness measurement method and device Download PDFInfo
- Publication number
- CN113884014B CN113884014B CN202111101314.7A CN202111101314A CN113884014B CN 113884014 B CN113884014 B CN 113884014B CN 202111101314 A CN202111101314 A CN 202111101314A CN 113884014 B CN113884014 B CN 113884014B
- Authority
- CN
- China
- Prior art keywords
- transfer function
- deconvolution
- signal
- metal substrate
- terahertz
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 239000002184 metal Substances 0.000 title claims abstract description 69
- 238000000576 coating method Methods 0.000 title claims abstract description 62
- 239000011248 coating agent Substances 0.000 title claims abstract description 57
- 239000000758 substrate Substances 0.000 title claims abstract description 50
- 238000000691 measurement method Methods 0.000 title claims abstract description 17
- 238000012546 transfer Methods 0.000 claims abstract description 95
- 239000000463 material Substances 0.000 claims abstract description 57
- 239000012925 reference material Substances 0.000 claims abstract description 41
- 238000005259 measurement Methods 0.000 claims abstract description 33
- 238000000034 method Methods 0.000 claims abstract description 26
- 230000009466 transformation Effects 0.000 claims description 7
- 230000008569 process Effects 0.000 claims description 5
- 238000004364 calculation method Methods 0.000 claims description 4
- 230000001131 transforming effect Effects 0.000 claims description 3
- 238000012216 screening Methods 0.000 claims description 2
- 238000002592 echocardiography Methods 0.000 abstract description 8
- 239000000523 sample Substances 0.000 description 9
- 238000010586 diagram Methods 0.000 description 8
- 238000001914 filtration Methods 0.000 description 6
- 239000010410 layer Substances 0.000 description 4
- 230000003287 optical effect Effects 0.000 description 4
- 239000013074 reference sample Substances 0.000 description 4
- 238000004587 chromatography analysis Methods 0.000 description 3
- 239000003989 dielectric material Substances 0.000 description 3
- 238000012360 testing method Methods 0.000 description 3
- 238000001514 detection method Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000007689 inspection Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 238000001328 terahertz time-domain spectroscopy Methods 0.000 description 2
- 229910000831 Steel Inorganic materials 0.000 description 1
- -1 archaeology Substances 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000000919 ceramic Substances 0.000 description 1
- 239000011247 coating layer Substances 0.000 description 1
- 238000009675 coating thickness measurement Methods 0.000 description 1
- 230000001427 coherent effect Effects 0.000 description 1
- 238000005260 corrosion Methods 0.000 description 1
- 230000032798 delamination Effects 0.000 description 1
- 230000001066 destructive effect Effects 0.000 description 1
- 239000003814 drug Substances 0.000 description 1
- 230000002708 enhancing effect Effects 0.000 description 1
- 239000006260 foam Substances 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000009659 non-destructive testing Methods 0.000 description 1
- 239000003973 paint Substances 0.000 description 1
- 230000000149 penetrating effect Effects 0.000 description 1
- 239000004033 plastic Substances 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 238000007789 sealing Methods 0.000 description 1
- 239000010959 steel Substances 0.000 description 1
- 238000002366 time-of-flight method Methods 0.000 description 1
- 238000009683 ultrasonic thickness measurement Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
- G01B11/02—Measuring arrangements characterised by the use of optical techniques for measuring length, width or thickness
- G01B11/06—Measuring arrangements characterised by the use of optical techniques for measuring length, width or thickness for measuring thickness ; e.g. of sheet material
- G01B11/0616—Measuring arrangements characterised by the use of optical techniques for measuring length, width or thickness for measuring thickness ; e.g. of sheet material of coating
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Length Measuring Devices By Optical Means (AREA)
Abstract
The invention provides a terahertz metal substrate multi-coating high-resolution thickness measurement method and device. The method comprises the steps that terahertz pulse waves respectively enter the surface of a metal substrate multi-coating material and the surface of a metal flat plate with the same height in the normal direction, and reflection echoes of a measured material and reflection echoes of a reference material are measured; judging whether the signal-to-noise ratio of the time domain echo signal of the measured material and the signal-to-noise ratio of the time domain echo signal of the reference material are higher than a set threshold value, if so, calculating to obtain a deconvolution transfer function of the time domain echo signal of the measured material and the time domain echo signal of the reference material in a frequency domain; according to the optimal parameters of the prediction model and the high signal-to-noise ratio basic data in the deconvolution transfer function, utilizing the forward prediction transfer function and the backward prediction transfer function to extrapolate the deconvolution transfer function outside the frequency range of the basic data to obtain a reconstructed full-frequency range deconvolution transfer function; and obtaining the multi-coating terahertz high-resolution thickness measurement data of the metal substrate based on the reconstructed full-frequency range deconvolution transfer function.
Description
Technical Field
The invention belongs to the field of metal substrate multi-coating thickness measurement, and particularly relates to a terahertz metal substrate multi-coating high-resolution thickness measurement method and device.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
Currently, common nondestructive testing methods for coating thickness mainly comprise a magnetic thickness measurement method, an eddy current thickness measurement method and an ultrasonic thickness measurement method. In most cases, these three methods require direct contact with the sample surface. Also, for multi-coating thickness detection, the ultrasonic method does not achieve sufficient thickness resolution due to the limitations of the operating frequency, and fails for thinner multi-coatings. Similarly, magnetic and eddy current thickness measurements also fail to provide a per layer thickness measurement of multiple coatings, and only the total thickness of all coatings on a metal substrate can be measured. In addition, magnetic and eddy current thickness measurement methods have poor lateral resolution and fail to provide accurate values of coating thickness for a single measurement point when multiple coating thicknesses are present in a small area.
Terahertz waves are located between microwaves and near infrared, the frequency ranges from 100GHz to 10THz, and the wavelength ranges from 3mm to 30 mu m, which are also called far infrared or submillimeter waves. Terahertz waves have many unique advantages over other electromagnetic waves:
(1) The photon energy is low. Terahertz waves do not cause harmful photoionization in biological tissues, and are safe to test materials and operators.
(2) The penetrating power is strong. Terahertz electromagnetic waves are longer than visible light and infrared wavelengths, and are not easily affected by Mie scattering. Most dry dielectrics such as foam, plastic, wood, ceramic, and the like can be penetrated.
(3) The thickness resolution is high. Terahertz waves can easily obtain the signal bandwidth of THz magnitude, and can provide thickness measurement resolution of micron magnitude.
Therefore, terahertz waves are very suitable for use in a safe, contactless, high-resolution, non-destructive inspection technique.
Terahertz time-of-flight chromatography technology, as a nondestructive, non-contact and non-ionization detection method, has been successfully applied to the fields of medicine, archaeology, steel products, safety inspection and the like. In particular, terahertz time-of-flight chromatography techniques have been used to measure the delamination thickness of multicoat paint systems.
After a single-period terahertz pulse is incident on a layered sample, due to the discontinuity of dielectrics, the interfaces of different dielectrics can generate reflection echoes, and the thickness of each layer of dielectric can be calculated based on the time delay of two echo pulses, and the measurement method is called a time-of-flight chromatography technology.
When the thickness of a single coating is too thin for terahertz waves, two reflected echoes of adjacent interfaces may be superimposed together on the time axis, resulting in indistinguishable corresponding single echoes. Currently, there have been many methods proposed for enhancing the thickness measurement capability of terahertz waves for thinner multi-coating layers. However, most belong to frequency domain filtering methods such as gaussian filtering, double gaussian filtering, wiener filtering, hanning window, etc. However, the filtering method not only can inhibit electric noise, but also can cause loss of useful signals, and the thickness measurement accuracy is affected.
Disclosure of Invention
In order to solve the technical problems in the background art, the invention provides a terahertz metal substrate multi-coating high-resolution thickness measurement method and device, which are used for reconstructing an expansion frequency band part of a signal by utilizing a limited high signal-to-noise ratio frequency band part in measurement data to obtain a multi-coating high-resolution measurement result.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
the first aspect of the invention provides a terahertz metal substrate multi-coating high-resolution thickness measurement method.
A terahertz metal substrate multi-coating high-resolution thickness measurement method comprises the following steps:
terahertz pulse waves are respectively incident on the surface of the metal substrate multi-coating material and the surface of the metal flat plate with the same height in the normal direction, and the reflected echo of the measured material and the reflected echo of the reference material are obtained through measurement; wherein the metal substrate multi-coating material is a measured material, and the metal flat plate is a reference material;
judging whether the signal-to-noise ratio of the time domain echo signal of the measured material and the signal-to-noise ratio of the time domain echo signal of the reference material are higher than a set threshold value, if so, calculating to obtain a deconvolution transfer function of the time domain echo signal of the measured material and the time domain echo signal of the reference material in a frequency domain;
determining a forward prediction transfer function and a backward prediction transfer function according to the selected optimal parameters of the prediction model;
according to the optimal parameters of the prediction model and the high signal-to-noise ratio basic data in the deconvolution transfer function, utilizing the forward prediction transfer function and the backward prediction transfer function to extrapolate the deconvolution transfer function outside the frequency range of the basic data to obtain a reconstructed full-frequency range deconvolution transfer function;
and obtaining the multi-coating terahertz high-resolution thickness measurement data of the metal substrate based on the reconstructed full-frequency range deconvolution transfer function.
Further, the process of judging whether the signal-to-noise ratio of the measured material time domain echo signal and the signal-to-noise ratio of the reference material time domain echo signal are higher than the set threshold value comprises the following steps: if not, adopting a denoising method to inhibit noise, so that the signal-to-noise ratio of the time domain echo signal of the measured material and the signal-to-noise ratio of the time domain echo signal of the reference material are higher than the set threshold.
Further, before the deconvolution transfer function calculation in the frequency domain, the method includes: and transforming the measured material time domain echo signal and the reference material time domain echo signal into a frequency domain by adopting Fourier transformation.
Further, before the determining the forward prediction transfer function and the backward prediction transfer function according to the selected prediction model optimal parameters, the method includes: and screening data with high signal to noise ratio in the deconvolution transfer function as basic data according to the set range.
Further, the optimal parameters of the prediction model include: order, forward coefficient, and backward coefficient.
Further, a prediction error function is obtained by combining the forward prediction transfer function and the backward prediction transfer function according to the order, the forward coefficient and the backward coefficient.
Further, the forward coefficient and the backward coefficient corresponding to the minimum value of the prediction error function are selected as the optimal forward coefficient and backward coefficient of the prediction model.
Further, obtaining an information entropy function of the order based on the number of data points and the order of the full frequency range in the prediction error function and the deconvolution transfer function of the frequency domain; and selecting the order corresponding to the minimum value of the information entropy function as the optimal order of the prediction model.
Further, the process for obtaining the metal substrate multi-coating terahertz high-resolution thickness measurement data based on the reconstructed full-frequency range deconvolution transfer function comprises the following steps: and carrying out Fourier inverse transformation on the reconstructed full-frequency range deconvolution transfer function to obtain multi-coating terahertz high-resolution thickness measurement data of the metal substrate.
The second aspect of the invention provides a terahertz metal substrate multi-coating high-resolution thickness measurement device.
A terahertz metal substrate multi-coating high-resolution thickness measurement device, comprising:
a reflected echo acquisition module configured to: terahertz pulse waves are respectively incident on the surface of the metal substrate multi-coating material and the surface of the metal flat plate with the same height in the normal direction, and the reflected echo of the measured material and the reflected echo of the reference material are obtained through measurement; wherein the metal substrate multi-coating material is a measured material, and the metal flat plate is a reference material;
a processor configured to: judging whether the signal-to-noise ratio of the time domain echo signal of the measured material and the signal-to-noise ratio of the time domain echo signal of the reference material are higher than a set threshold value, if so, calculating to obtain a deconvolution transfer function of the time domain echo signal of the measured material and the time domain echo signal of the reference material in a frequency domain; determining a forward prediction transfer function and a backward prediction transfer function according to the selected optimal parameters of the prediction model; according to the optimal parameters of the prediction model and the high signal-to-noise ratio basic data in the deconvolution transfer function, utilizing the forward prediction transfer function and the backward prediction transfer function to extrapolate the deconvolution transfer function outside the frequency range of the basic data to obtain a reconstructed full-frequency range deconvolution transfer function; and obtaining the multi-coating terahertz high-resolution thickness measurement data of the metal substrate based on the reconstructed full-frequency range deconvolution transfer function.
Compared with the prior art, the invention has the beneficial effects that:
according to the frequency domain data of the measured sample and the reference sample, a frequency domain deconvolution filter is obtained; optimizing prediction model parameters such as an order, a forward coefficient, a backward coefficient and the like according to a prediction error minimum principle and an information entropy criterion, reconstructing an expansion band part of a signal based on a limited high signal-to-noise ratio band part in measured data, and obtaining multi-coating terahertz high-resolution thickness measured data of a metal substrate by using Fourier inverse transformation; the method avoids the phenomenon of effective signal loss caused by the traditional frequency domain filtering method, and can more accurately obtain the terahertz high-resolution thickness measurement result of the multi-coating sample.
Additional aspects of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention.
FIG. 1 is a flow chart of a terahertz metal substrate multi-coating high-resolution thickness measurement method shown in the invention;
FIG. 2 is a sample hierarchy to be tested shown in the present invention;
FIG. 3 is a schematic diagram of the echo of a sample under test according to the present invention;
FIG. 4 is a schematic diagram of a reference sample echo shown in the present invention;
FIG. 5 is a schematic representation of a segment of the present invention for calculating the signal-to-noise ratio of the time domain echo signals of the measured material and the reference material;
FIG. 6 is a graph comparing an original terahertz reflected pulse echo signal with a processed echo signal shown in the present invention;
fig. 7 is a block diagram of a terahertz time-domain spectroscopy apparatus shown in the present invention.
Detailed Description
The invention will be further described with reference to the drawings and examples.
It should be noted that the following detailed description is illustrative and is intended to provide further explanation of the invention. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present invention. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
It is noted that the flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of methods and systems according to various embodiments of the present disclosure. It should be noted that each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the logical functions specified in the various embodiments. It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by special purpose hardware-based systems which perform the specified functions or operations, or combinations of special purpose hardware and computer instructions.
Example 1
The embodiment provides a terahertz metal substrate multi-coating high-resolution thickness measurement method.
As shown in fig. 1, a terahertz metal substrate multi-coating high-resolution thickness measurement method includes:
terahertz pulse waves are respectively incident on the surface of the metal substrate multi-coating material and the surface of the metal flat plate with the same height in the normal direction, and the reflected echo of the measured material and the reflected echo of the reference material are obtained through measurement; wherein the metal substrate multi-coating material is a measured material, and the metal flat plate is a reference material;
judging whether the signal-to-noise ratio of the time domain echo signal of the measured material and the signal-to-noise ratio of the time domain echo signal of the reference material are higher than a set threshold value, if so, calculating to obtain a deconvolution transfer function of the time domain echo signal of the measured material and the time domain echo signal of the reference material in a frequency domain;
determining a forward prediction transfer function and a backward prediction transfer function according to the selected optimal parameters of the prediction model;
according to the optimal parameters of the prediction model and the high signal-to-noise ratio basic data in the deconvolution transfer function, utilizing the forward prediction transfer function and the backward prediction transfer function to extrapolate the deconvolution transfer function outside the frequency range of the basic data to obtain a reconstructed full-frequency range deconvolution transfer function;
and obtaining the multi-coating terahertz high-resolution thickness measurement data of the metal substrate based on the reconstructed full-frequency range deconvolution transfer function.
Specifically, the following scheme may be adopted in the specific process of implementation:
terahertz time-domain spectroscopy instruments are used for transmitting and receiving terahertz time-domain pulses, and an instrument structure diagram is shown in fig. 7. The device comprises an ultrafast laser, a beam splitter, a reflecting mirror, an optical delay device, a photoconductive antenna transmitter, a photoconductive antenna receiver, a processor and other modules. The ultra-fast laser emits ultra-short light pulse, the ultra-short light pulse is divided into two beams through the beam splitter, one beam is converted into terahertz pulse wave through the photoconductive antenna emitter and is emitted to the surface of the object to be measured, and the other beam enters the optical delay through the reflecting mirror. The processor can control the optical delay device to realize step delay, so that the output signal of the optical delay device and the terahertz reflection echo received by the photoconductive antenna receiver are scanned and coherent along a time axis, and then the equivalent sampling principle is utilized to obtain the terahertz reflection echo digital signal. In the embodiment, the effective frequency band of the terahertz time-domain pulse is 60 GHz-3 THz, and the average output power is 65mW.
Using a flat automobile plate as a tested sample, wherein the tested sample comprises a metal layer, an anti-corrosion coating, a sealing coating, a surface coating and other 4-layer structures, as shown in figure 2; a metal flat plate with a high reflectance was used as a reference sample.
Step 1, terahertz pulse waves are incident to the surface of a metal substrate multi-coating material in a normal direction, and reflection echoes of the measured material are measured and recorded as i (t); and placing a metal flat plate at the same height as the metal substrate, taking the metal flat plate as a reference sample, enabling terahertz pulse waves to be incident on the surface of the metal flat plate in the normal direction, and measuring to obtain a reflection echo of the reference material, wherein the reflection echo is denoted as r (t). The i (t) signal contains the reflected echoes of all 4 different material interfaces of the sample under test, as shown in FIG. 3; the r (t) signal contains only a single reflected echo of the surface of the metal plate, as shown in fig. 4.
Step 2, calculating the signal-to-noise ratio of the time domain echo signals of the measured material and the reference material by using a signal-to-noise ratio calculation method facing the time domain pulse:
wherein SNR is i Sum SNR r Echo signal-to-noise ratio, i, of the measured material and the reference material, i max And i min Respectively represent the maximum value and the minimum value of the time domain echo pulse signal of the measured material, such as the highest point and the lowest point of the section b in fig. 5, STD (i a ) A standard deviation of a region without an effective signal before a first reflection peak in the time domain echo pulse signal of the measured material is shown as a region a in fig. 5; r is (r) max And r min Respectively representing the maximum and minimum values of the time-domain echo pulse signal of the reference material, such as the highest point and the lowest point of section b in fig. 5, STD (r a ) Representing the standard deviation of the region of the reference material time domain echo pulse signal where there is no effective signal before the first reflection peak, as in FIG. 5Is defined as region a.
And step 3, judging whether the signal-to-noise ratio of the time domain echo signals of the measured material and the reference material is high enough. Typically the threshold for signal to noise ratio is chosen to be 20dB. If the signal-to-noise ratio of the echo signal is higher than the threshold value, the data directly enter the next step of processing; if the signal-to-noise ratio of the echo signal is below the threshold, the noise is suppressed by using a conventional denoising method, such as wavelet transform denoising, so that the signal-to-noise ratio of the echo signal is above the threshold.
Step 4, using Fourier transformation, transforming the time domain echo signals of the measured material and the reference material into a frequency domain:
step 5, using R (f) divided by I (f), calculating to obtain a deconvolution transfer function of the frequency domain:
step 6, according to H inv (f) As a result, select H inv (f) The high SNR part is selected according to the ratio so that the high SNR frequency range occupies H inv (f) About 70% of the total frequency range. The lower limit of the frequency range of the high signal-to-noise ratio part is marked as f low The upper limit of the frequency range of the high signal-to-noise ratio part is marked as f high 。
And 7, selecting optimal parameters of the prediction model, wherein the optimal parameters comprise an order p, a forward coefficient a and a backward coefficient b. Typically, the value of the order p ranges from 1 to 15% of the total frequency points. The forward coefficient a and the backward coefficient b are a group of sequences, the number of elements of the sequences is consistent with the value of the order p, and the sum of all elements is 1. The closer the earlier element in the sequence is to 1, the closer the later element in the sequence is to 0. For simplicity, the differences between adjacent elements in the sequence may be set to be constant, denoted as Δa and Δb.
Thus, the forward coefficient a and the backward coefficient b can be expressed as:
a＝{a min ,a min +Δa,a min +2Δa,…,a min +(p-1)Δa}
b＝{b min ,b min +Δb,b min +2Δb,…,b min +(p-1)Δb}
wherein a is min And b min Respectively represent the minimum value, a in the sequence min ++ (p-1) Δa and b min All + (p-1) Δb are less than 1, and Σa= Σb=1.
Determining a forward prediction transfer function and a backward prediction transfer function according to the order p, the forward coefficient a and the backward coefficient b:
wherein a is k And b k Representing the kth element in the a and b sequences, respectively. The meaning of the formula can be described as: currently calculated forward transfer functionBy->The previous p transfer function values H ia-k (k=1, 2,3 … p) together, the currently calculated backward transfer function +.>By->The p transfer function values thereafterH ib+k (k=1, 2,3 … p). The forward predictive transfer function and the backward predictive transfer function together form a predictive model.
For each specific order p value, various possible combinations of a and b sequences are traversed. To save traversal time, the values of the a and b sequences may be defined to be integer multiples of 0.01. From the sequences of the orders p, a and b, the forward and backward prediction transfer functions, and the prediction error, can be calculated:
wherein i is low And i high Represents H inv (f) The positions of the lowest frequency and the highest frequency elements in the basic data of the medium-high signal-to-noise ratio, i plow ＝i low +p-1，i phigh ＝i high -p+1。
For each particular order p value, a prediction error is selectedThe smallest a and b sequences combine as the only a and b sequences for this p value.
Calculating an information entropy criterion corresponding to each order p value:
wherein N represents H inv (f) Data points for the full frequency range.
Selection information entropy E p And the p value corresponding to the minimum value is used as the optimal order of the prediction model. To this end, 3 parameters of the order p, the forward coefficient a and the backward coefficient b have been determined.
Step 8, according to the optimal parameter order p of the prediction model, the forward coefficient a, the backward coefficient b and H inv (f) The basic data with medium and high signal to noise ratio is extrapolated to obtain the deconvolution outside the frequency range of the basic data by utilizing the forward prediction transfer function and the backward prediction transfer function calculation formulaTransfer function, obtaining reconstructed full-frequency range deconvolution transfer function
Step 9, deconvolution transfer function for full frequency rangeAnd performing Fourier inverse transformation to obtain multi-coating terahertz high-resolution thickness measurement data of the metal substrate.
The original terahertz reflection pulse echo signal and the echo signal processed by the method are shown in fig. 6. Because the bandwidth of the terahertz pulse signal is limited, for thinner coatings, the original signal can have pulse fusion phenomena, such as 1 st and 2 nd reflection pulses, so that the resolution of measuring and calculating the thickness of the coating by using a time-of-flight method is affected, as shown by a dotted line; the method provided by the invention is used for processing the original signals, so that 4 echoes can be very narrow, as shown by solid lines, and the thickness measurement resolution is improved.
Example two
The embodiment provides a terahertz metal substrate multi-coating high-resolution thickness measurement device.
A terahertz metal substrate multi-coating high-resolution thickness measurement device, comprising:
a reflected echo acquisition module configured to: terahertz pulse waves are respectively incident on the surface of the metal substrate multi-coating material and the surface of the metal flat plate with the same height in the normal direction, and the reflected echo of the measured material and the reflected echo of the reference material are obtained through measurement; wherein the metal substrate multi-coating material is a measured material, and the metal flat plate is a reference material;
a processor configured to: judging whether the signal-to-noise ratio of the time domain echo signal of the measured material and the signal-to-noise ratio of the time domain echo signal of the reference material are higher than a set threshold value, if so, calculating to obtain a deconvolution transfer function of the time domain echo signal of the measured material and the time domain echo signal of the reference material in a frequency domain; determining a forward prediction transfer function and a backward prediction transfer function according to the selected optimal parameters of the prediction model; according to the optimal parameters of the prediction model and the high signal-to-noise ratio basic data in the deconvolution transfer function, utilizing the forward prediction transfer function and the backward prediction transfer function to extrapolate the deconvolution transfer function outside the frequency range of the basic data to obtain a reconstructed full-frequency range deconvolution transfer function; and obtaining the multi-coating terahertz high-resolution thickness measurement data of the metal substrate based on the reconstructed full-frequency range deconvolution transfer function.
It should be noted that the above-mentioned reflected echo acquiring module and processor are the same as the example and application implemented by the steps in the first embodiment, but are not limited to the disclosure of the first embodiment. It should be noted that the modules described above may be implemented as part of a system in a computer system, such as a set of computer-executable instructions.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (6)
1. A terahertz metal substrate multi-coating high-resolution thickness measurement method is characterized by comprising the following steps of:
terahertz pulse waves are respectively incident on the surface of the metal substrate multi-coating material and the surface of the metal flat plate with the same height in the normal direction, and the reflected echo of the measured material and the reflected echo of the reference material are obtained through measurement; wherein the metal substrate multi-coating material is a measured material, and the metal flat plate is a reference material;
judging whether the signal-to-noise ratio of the time domain echo signal of the measured material and the signal-to-noise ratio of the time domain echo signal of the reference material are higher than a set threshold value, if so, calculating to obtain a deconvolution transfer function of the time domain echo signal of the measured material and the time domain echo signal of the reference material in a frequency domain;
determining a forward prediction transfer function and a backward prediction transfer function according to the selected optimal parameters of the prediction model;
the optimal parameters of the prediction model comprise: model order, forward coefficient and backward coefficient;
according to the order, the forward coefficient and the backward coefficient, combining the forward prediction transfer function and the backward prediction transfer function to obtain a prediction error function;
selecting a forward coefficient and a backward coefficient corresponding to the minimum value of the prediction error function as the optimal forward coefficient and backward coefficient;
obtaining an information entropy function of the order based on the number of data points and the order of the full frequency range in the prediction error function and the deconvolution transfer function of the frequency domain; selecting the order corresponding to the minimum value of the information entropy function as the optimal order;
according to the optimal parameters of the prediction model and the high signal-to-noise ratio basic data in the deconvolution transfer function, utilizing the forward prediction transfer function and the backward prediction transfer function to extrapolate the deconvolution transfer function outside the frequency range of the basic data to obtain a reconstructed full-frequency range deconvolution transfer function;
and obtaining the multi-coating terahertz high-resolution thickness measurement data of the metal substrate based on the reconstructed full-frequency range deconvolution transfer function.
2. The method for measuring the thickness of the terahertz metal substrate with multiple coatings at high resolution according to claim 1, wherein the process of determining whether the signal-to-noise ratio of the time-domain echo signal of the measured material and the signal-to-noise ratio of the time-domain echo signal of the reference material are both higher than a set threshold value comprises: if not, adopting a denoising method to inhibit noise, so that the signal-to-noise ratio of the time domain echo signal of the measured material and the signal-to-noise ratio of the time domain echo signal of the reference material are higher than the set threshold.
3. The terahertz metal substrate multi-coating high-resolution thickness measurement method according to claim 1, characterized by comprising, before the calculation of the deconvolution transfer function in the frequency domain: and transforming the measured material time domain echo signal and the reference material time domain echo signal into a frequency domain by adopting Fourier transformation.
4. The terahertz metal substrate multi-coating high-resolution thickness measurement method according to claim 1, characterized by comprising, before the determination of forward and backward predicted transfer functions according to the selected prediction model optimal parameters: and screening data with high signal to noise ratio in the deconvolution transfer function as basic data according to the set range.
5. The method for measuring the terahertz metal substrate multi-coating high-resolution thickness according to claim 1, wherein the process for obtaining the metal substrate multi-coating terahertz high-resolution thickness measurement data based on the reconstructed full-frequency range deconvolution transfer function comprises the following steps: and carrying out Fourier inverse transformation on the reconstructed full-frequency range deconvolution transfer function to obtain multi-coating terahertz high-resolution thickness measurement data of the metal substrate.
6. A terahertz metal substrate multi-coating high-resolution thickness measurement device, characterized by comprising:
a reflected echo acquisition module configured to: terahertz pulse waves are respectively incident on the surface of the metal substrate multi-coating material and the surface of the metal flat plate with the same height in the normal direction, and the reflected echo of the measured material and the reflected echo of the reference material are obtained through measurement; wherein the metal substrate multi-coating material is a measured material, and the metal flat plate is a reference material;
a processor configured to: judging whether the signal-to-noise ratio of the time domain echo signal of the measured material and the signal-to-noise ratio of the time domain echo signal of the reference material are higher than a set threshold value, if so, calculating to obtain a deconvolution transfer function of the time domain echo signal of the measured material and the time domain echo signal of the reference material in a frequency domain; determining a forward prediction transfer function and a backward prediction transfer function according to the selected optimal parameters of the prediction model; the optimal parameters of the prediction model comprise: model order, forward coefficient and backward coefficient; according to the order, the forward coefficient and the backward coefficient, combining the forward prediction transfer function and the backward prediction transfer function to obtain a prediction error function; selecting a forward coefficient and a backward coefficient corresponding to the minimum value of the prediction error function as the optimal forward coefficient and backward coefficient; obtaining an information entropy function of the order based on the number of data points and the order of the full frequency range in the prediction error function and the deconvolution transfer function of the frequency domain; selecting the order corresponding to the minimum value of the information entropy function as the optimal order; according to the optimal parameters of the prediction model and the high signal-to-noise ratio basic data in the deconvolution transfer function, utilizing the forward prediction transfer function and the backward prediction transfer function to extrapolate the deconvolution transfer function outside the frequency range of the basic data to obtain a reconstructed full-frequency range deconvolution transfer function; and obtaining the multi-coating terahertz high-resolution thickness measurement data of the metal substrate based on the reconstructed full-frequency range deconvolution transfer function.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111101314.7A CN113884014B (en) | 2021-09-18 | 2021-09-18 | Terahertz metal substrate multi-coating high-resolution thickness measurement method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111101314.7A CN113884014B (en) | 2021-09-18 | 2021-09-18 | Terahertz metal substrate multi-coating high-resolution thickness measurement method and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113884014A CN113884014A (en) | 2022-01-04 |
| CN113884014B true CN113884014B (en) | 2024-02-27 |
Family
ID=79010049
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111101314.7A Active CN113884014B (en) | 2021-09-18 | 2021-09-18 | Terahertz metal substrate multi-coating high-resolution thickness measurement method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113884014B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114486179B (en) * | 2022-02-21 | 2023-06-13 | 电子科技大学中山学院 | High-precision optical fiber quality detection method and system for deconvolution filtering |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101621366B1 (en) * | 2015-03-30 | 2016-05-17 | 한양대학교 산학협력단 | DEVICE AND METHOD FOR MEASURING DIELECTRIC CONSTANT AND DAMPED RATIO OF MATTER USING THz TRANSFER FUNCTION MODEL IN THz DOMAIN |
| CN106596462A (en) * | 2016-12-19 | 2017-04-26 | 中国矿业大学 | Paper sheet quantitative detection method based on terahertz phase shifting characteristics and particle swarm algorithm |
| CN110554049A (en) * | 2019-09-23 | 2019-12-10 | 清华大学深圳国际研究生院 | composite insulator defect detection device and method based on terahertz wave, and medium |
| CN111998783A (en) * | 2020-07-13 | 2020-11-27 | 北京工业大学 | Reflection type terahertz time-domain spectrum thickness measurement method based on evolutionary optimization algorithm |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR102266644B1 (en) * | 2013-11-15 | 2021-06-18 | 루나 이노베이션스 인코퍼레이티드 | A system for determining at least one property of a sheet dielectric sample using terahertz radiation |
-
2021
- 2021-09-18 CN CN202111101314.7A patent/CN113884014B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101621366B1 (en) * | 2015-03-30 | 2016-05-17 | 한양대학교 산학협력단 | DEVICE AND METHOD FOR MEASURING DIELECTRIC CONSTANT AND DAMPED RATIO OF MATTER USING THz TRANSFER FUNCTION MODEL IN THz DOMAIN |
| CN106596462A (en) * | 2016-12-19 | 2017-04-26 | 中国矿业大学 | Paper sheet quantitative detection method based on terahertz phase shifting characteristics and particle swarm algorithm |
| CN110554049A (en) * | 2019-09-23 | 2019-12-10 | 清华大学深圳国际研究生院 | composite insulator defect detection device and method based on terahertz wave, and medium |
| CN111998783A (en) * | 2020-07-13 | 2020-11-27 | 北京工业大学 | Reflection type terahertz time-domain spectrum thickness measurement method based on evolutionary optimization algorithm |
Non-Patent Citations (3)
| Title |
|---|
| 基于修正自相关算法的THz-TDS聚乙烯测厚反卷积算法;蒋强;俞跃;叶凌伟;郝元;;红外技术(05);全文 * |
| 太赫兹时域光谱反射式层析成像技术;任姣姣;李丽娟;张丹丹;乔晓利;;红外与激光工程(02);全文 * |
| 太赫兹时域光谱测量液体光学常数的不确定度分析;张放;杨霏;;中国测试(05);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113884014A (en) | 2022-01-04 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US7612341B2 (en) | Image of sample using terahertz time domain spectroscopy in reflection mode to identify in a first material like normal breast tissue a second material like cancerous tissue by evaluating the phase change at the interface between the sample and a window like a quartz window, against which the sample is pressed | |
| CN111998783A (en) | Reflection type terahertz time-domain spectrum thickness measurement method based on evolutionary optimization algorithm | |
| Pochanin et al. | Advances in short-range distance and permittivity ground-penetrating radar measurements for road surface surveying | |
| JP2008545123A (en) | Method and system for determining material properties using ultrasonic attenuation | |
| JPH11108845A (en) | Method for providing composition image of inspection object in real time | |
| CN113884014B (en) | Terahertz metal substrate multi-coating high-resolution thickness measurement method and device | |
| US11085874B2 (en) | Characterization of multilayer structures | |
| Zhai et al. | Thickness characterization of multi-layer coated steel by terahertz time-of-flight tomography | |
| CN112162295A (en) | Terahertz thickness detection optimization method based on time-frequency analysis | |
| Liu et al. | Synchrosqueezed wavelet transform-based method for characterizing the dispersive nature of laser-excited surface acoustic waves propagating through the coated or damaged medium | |
| CN112462356A (en) | Method for improving radar ranging precision based on Gaussian interpolation | |
| CN114427838A (en) | Method and system for predicting and evaluating thickness of medium based on reflection terahertz spectrum | |
| WO2016203049A1 (en) | Characterization of multilayer structures | |
| CN116482690A (en) | Method for evaluating measurement accuracy of ship RCS by ship-borne phased array radar | |
| Murav’eva et al. | Analysis of reflected signals in testing cylindrical specimens by the multiple reflection echo-shadow method | |
| EP3311147A1 (en) | Characterization of multilayer structures | |
| CN115326745A (en) | Nondestructive testing method for THz time domain spectrum of electric power safety tool | |
| Sun et al. | Time delay and interface roughness estimations by GPR for pavement survey | |
| Abouzaid et al. | FMCW radar-based material characterization using convolutional neural network and K-means clustering | |
| CN110726995A (en) | Laser radar high-precision ranging method and system | |
| Ma et al. | Thickness determination of dual-layer coatings based on ultrasonic spectral filtering | |
| Gust et al. | Deconvolution of ultrasonic signals in time domain | |
| Liu et al. | Accurate Thickness Measurement Based on Dispersion Compensation via Terahertz Time-Domain Spectroscopy | |
| CN115790408A (en) | Time domain spectroscopy technology-based method for estimating thickness of adhesive layer of ceramic metal adhesive structure | |
| Li et al. | Separation of multi-echo overlapping ultrasonic signals for increasing the axial resolution using a neural network |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |