CN111220564A - Terahertz detection optimization method for bonding pressurization parameters of multilayer structure - Google Patents
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- 238000005457 optimization Methods 0.000 title claims abstract description 20
- 238000012360 testing method Methods 0.000 claims abstract description 75
- 238000003384 imaging method Methods 0.000 claims abstract description 42
- 239000011153 ceramic matrix composite Substances 0.000 claims abstract description 17
- 238000012706 support-vector machine Methods 0.000 claims abstract description 15
- 239000000463 material Substances 0.000 claims abstract description 12
- 238000012795 verification Methods 0.000 claims abstract description 6
- 239000010410 layer Substances 0.000 claims description 35
- VYPSYNLAJGMNEJ-UHFFFAOYSA-N Silicium dioxide Chemical compound O=[Si]=O VYPSYNLAJGMNEJ-UHFFFAOYSA-N 0.000 claims description 24
- 239000000741 silica gel Substances 0.000 claims description 24
- 229910002027 silica gel Inorganic materials 0.000 claims description 24
- 239000012790 adhesive layer Substances 0.000 claims description 7
- 238000001228 spectrum Methods 0.000 claims description 4
- 238000000701 chemical imaging Methods 0.000 claims description 3
- 238000004519 manufacturing process Methods 0.000 claims description 3
- 239000000919 ceramic Substances 0.000 claims description 2
- XUIMIQQOPSSXEZ-UHFFFAOYSA-N Silicon Chemical compound [Si] XUIMIQQOPSSXEZ-UHFFFAOYSA-N 0.000 claims 4
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- 239000010703 silicon Substances 0.000 claims 4
- 239000000758 substrate Substances 0.000 claims 1
- 238000004364 calculation method Methods 0.000 description 7
- 238000010586 diagram Methods 0.000 description 5
- 229920002379 silicone rubber Polymers 0.000 description 5
- 239000004945 silicone rubber Substances 0.000 description 5
- 239000011159 matrix material Substances 0.000 description 4
- 238000003825 pressing Methods 0.000 description 4
- 239000003292 glue Substances 0.000 description 3
- 239000000853 adhesive Substances 0.000 description 2
- 230000001070 adhesive effect Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 239000002184 metal Substances 0.000 description 2
- 238000004026 adhesive bonding Methods 0.000 description 1
- 238000007718 adhesive strength test Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000009413 insulation Methods 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000009659 non-destructive testing Methods 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 230000002265 prevention Effects 0.000 description 1
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- 238000001328 terahertz time-domain spectroscopy Methods 0.000 description 1
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- G01N21/3586—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using far infrared light; using Terahertz radiation by Terahertz time domain spectroscopy [THz-TDS]
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- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
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Abstract
The invention discloses a terahertz detection optimization method for a bonding pressurization parameter of a multilayer structure, which designs and manufactures two groups of different pressurization time t1、t2The multilayer structure is bonded with a test piece, waveform characteristic values of the test piece are extracted, and terahertz detection is respectively carried out on the two groups of test pieces; carrying out multi-eigenvalue imaging and bonding strength test on waveform data obtained by terahertz detection; establishing a prediction model by using a support vector machine; additionally, a set of pressurization times t is designed and manufactured3The test sample piece carries out verification optimization on the prediction model; the optimized prediction model is used for classifying and optimizing the pressurization parameters of the ceramic matrix composite material bonding process.
Description
Technical Field
The invention relates to a terahertz detection optimization method for a bonding pressurization parameter of a multilayer structure, which is used for optimizing a pressurization time process parameter in the bonding of a ceramic matrix composite material and belongs to the technical field of nondestructive testing.
Background
The ceramic matrix composite has the characteristics of ultra-light weight, high porosity, low heat conduction and integration of heat prevention and insulation, and is connected with a bonding matrix structure in an organic adhesion mode in use, wherein the ceramic matrix composite and the bonding matrix are relatively fragile due to different thermal expansion coefficients, and a buffer cushion layer needs to be added to serve as a buffer, namely the combined use state of the ceramic matrix composite, the organic glue, the buffer cushion, the organic glue and the bonding matrix is realized.
In the use process of the organic adhesive, in order to ensure the bonding strength between the organic adhesive and a bonding matrix, process parameters are firstly searched, and the actual bonding process parameters are finally determined by combining the experimental data of the bonding strength test of the bonding test block. The existing bonding process technology mainly comprises surface treatment of a bonding surface, glue preparation, gluing, airing, pressure bonding and normal-temperature curing. The most common means in pressure bonding is vacuum pressure, and the vacuum pressure time is two most important technical indexes influencing the bonding strength. Different groups of bonding strength tests with different vacuum pressurization time variables are required to be set, and the reliability of the bonding process is analyzed by using the bonding strength assessment indexes.
Disclosure of Invention
The invention aims to introduce a terahertz time-domain spectroscopy technology into the determination of the bonding process parameters of the ceramic matrix composite material, provide a terahertz detection data analysis method for a bonding test block of pressurization time, and jointly optimize the bonding process parameters by combining the bonding strength test experimental data of the bonding test block.
The purpose of the invention is realized by the following technical scheme:
a multilayer structure bonding pressurization parameter terahertz detection optimization method is characterized by comprising the following steps:
step one, designing and manufacturing two groups of different pressurizing time t1、t2The test piece is bonded with the multilayer structure, the waveform characteristic value of the test piece is extracted, terahertz detection is respectively carried out on the two groups of test pieces, terahertz time-domain spectral imaging is carried out on detection data, and a terahertz time-domain atlas of the test piece is obtained;
step two, carrying out multi-eigenvalue imaging on the waveform data obtained by the terahertz detection in the step one to obtain eigenvalues of different imaging modes;
thirdly, performing bonding strength tests on the test pieces subjected to terahertz detection and different pressurization times, and comparing the actual tensile strength of the adhesive layers of the test pieces subjected to terahertz detection and different pressurization times under the condition of different pressurization times;
step four, establishing a prediction model by using a support vector machine: inputting the characteristic values of different imaging modes obtained by imaging of the multiple characteristic values in the step two and the bonding strength test data of the test pieces with different pressurizing times obtained in the step three into a support vector machine to establish a prediction model;
step five, verifying and optimizing the prediction model established in the step four: additionally, a set of pressurization times t is designed and manufactured3The terahertz detection imaging is utilized to extract characteristic values of different imaging modes, and the characteristic values of the different imaging modes are input into the prediction model established in the step four to obtain a bonding strength prediction result; for the pressurizing time t3Carrying out a bonding strength test on the test sample to obtain a test result, judging the consistency of a prediction result obtained by the prediction model and an actual bonding strength result by using the test result, and optimizing the prediction model; the optimized prediction model is used for classifying and optimizing the pressurization parameters of the ceramic matrix composite material bonding process.
Drawings
FIG. 1 is a schematic flow chart of a multilayer structure bonding pressurization parameter terahertz detection optimization method.
Fig. 2 is a terahertz time-domain waveform explanatory diagram.
FIG. 3 is a schematic diagram of a support vector machine.
FIG. 4 is a diagram of support vector machine classification results.
FIG. 5 is a structural view of a bonding test piece.
FIG. 6 is a schematic diagram of an optimization model.
Detailed Description
The technical scheme of the invention is described in detail in the following with reference to the attached drawings and embodiments:
the principle of the invention is that firstly, two groups of comparison tests with different pressurizing time are designed and manufactured, the characteristic values of different imaging modes of a sample piece are extracted from test data by combining terahertz waves, actual bonding strength data are obtained by combining a bonding strength test, an optimization model is established by using the characteristic values of different imaging modes of the two groups of test data, a bonding strength result is predicted, and finally, a group of pressurizing time is t3The test piece is used for verification, whether the predicted value of the optimized model and the actual bonding strength value are in accordance is judged, and if so, the model can be used for the classification and optimization method of the pressurization parameters of the ceramic matrix composite bonding process. If not, adjusting the optimization model to predict the bonding strength value until the bonding strength value is in accordance with the actual bonding strength value.
Referring to fig. 2, a typical terahertz wave propagation condition in a sample piece layered medium is labeled, in which an interface ① is a tile-upper surface layered interface of a ceramic matrix composite material bonding test piece, a region ② is a first silicone rubber layer region, a region ③ is a cushion pad region, a region ④ is a second silicone rubber layer region, and an interface ⑤ is an interface where a second silicone rubber layer is bonded to a metal plate.
As shown in fig. 1, the terahertz detection optimization method for the bonding pressurization parameter of the multilayer structure comprises the following steps:
step one, designing and manufacturing test samples with different pressurization times, and predicting and designing 2 groups of ceramic matrix composite material bonding test pieces with different pressurization times, wherein the specific structure is shown in figure 5, the 1 st layer is a ceramic-based material, the 2 nd layer is a first organic silica gel layer, the 3 rd layer is a cushion pad, the 4 th layer is a second organic silica gel layer, and the 5 th layer is a metal plate (bonding base body). The following characteristic values were extracted for the test pieces: first of allThe energy, the variation coefficient, the variance and the flight time of the organic silica gel layer, the energy, the kurtosis, the variance and the flight time of the second organic silica gel layer, the energy and the variance of the cushion pad and the flight time from the first organic silica gel layer to the second organic silica gel layer. The pressurizing time is respectively t1、t2After the test pieces are manufactured, terahertz detection is respectively carried out on the two groups of test pieces to obtain terahertz waveforms with the time window length T, and terahertz time-domain waveform amplitudes E (T) at all detection points. And then carrying out terahertz time-domain spectral imaging on the test pieces with different pressurizing times to obtain terahertz time-domain spectrograms of the test pieces with different pressurizing times.
Step two, performing multi-eigenvalue imaging on the waveform data of the ceramic matrix composite material bonding test piece obtained in the step one, performing imaging in different modes aiming at different regions of the test piece, and performing power spectrum imaging IM (instant messaging) on the first organic silica gel layer, the second organic silica gel layer and the buffer padup，IMdown，IMdnSum variance imaging Vupσ，Vdownσ，VdnσSeparately performing coefficient of variation imaging on the first organic silica gel layer CvThe second organic silica gel layer is subjected to kurtosis imaging K, and time-of-flight imaging T is carried out on the upper surface of the first organic silica gel layer, the lower surface of the second organic silica gel layer, the upper surface of the first organic silica gel layer and the lower surface of the second organic silica gel layertup，Ttdown，TtTherefore, the characteristic value imaging information of different areas of the test piece is obtained, and further the characteristic values of different imaging modes are obtained.
The power spectrum imaging calculation formula is as follows (1):
wherein, T is a terahertz time-domain waveform power spectrum imaging calculation interval, and e (T) is a signal amplitude corresponding to the flight time T in the terahertz time-domain waveform.
The variance imaging calculation formula is as follows (2):
where N is the number of index samples, E (t)i) For the time of flight T in the terahertz time-domain waveformiThe corresponding amplitude.
And Avg is the average value of sampling points in the T interval. T isumIs the maximum value E of the amplitude in the interval Tmax(T) corresponding time-of-flight value, Tum＝{Ti|max(E(Ti)),Ti∈TupAnd ω (t) is a Gaussian window function.
The calculation formula of the flight time imaging is as follows (3):
Tt＝T2-T1(3)
wherein, T2For the corresponding time of flight, T, of the lower surface of the imaging area1The time of flight corresponding to the upper surface of the imaging area.
The kurtosis imaging calculation formula is as follows (4):
wherein, E (t)i) For the time of flight T in the terahertz time-domain waveformiThe corresponding amplitude. T isCalThe method is used for extracting a calculation interval with obvious change of the waveform kurtosis characteristic value.
The calculation formula of the coefficient of variation is as follows (5):
Cv＝(σ(T)÷E(t))*100％ (5)
wherein σ (T) is a standard deviation of the amplitude of the waveform signal of the first silicone rubber layer, and e (T) is an average value of the amplitude of the waveform signal of the first silicone rubber layer.
Step three, carrying out bonding strength tests on the ceramic matrix composite bonding test pieces with different pressing time obtained in the step two to obtain different pressing time t1，t2The adhesive strength test data of (2) are used to compare the tensile strength of the adhesive layer of the test piece at different pressing times.
Step four, inputting the characteristic values of different imaging modes obtained by imaging of the multiple characteristic values in the step two and the bonding strength test result obtained in the step three into a support vector machine, and establishing a prediction model by using the support vector machine, wherein the prediction model is shown as figure 6, and the prediction function is as follows:
H1:(ω,x)+b＝-1
H0:(ω,x)+b＝0
H2:(ω,x)+b＝1
yi((ω,xi)+b)≥1
from the above classification criteria, a classification decision function (set ω)*，b*Is the optimal solution of the model)
f(x)＝sgn((ω*,x)+b*)
Where f (x) is the result of model prediction.
And (3) internal verification of the prediction model: FIG. 3 shows a schematic diagram of a support vector machine, which shows different pressurizing times t obtained in step two1，t2The characteristic values of different imaging modes of the test piece are input into a support vector machine, a prediction model is established by the support vector machine, and the predicted bonding strength values of the test piece at different pressurizing time are obtained through the prediction model. The adhesion strength tests of the test pieces with different pressing times are classified by means of a support vector machine, and the specific classification result is shown in fig. 4. And (3) comparing the predicted bonding strength values of the test pieces with different pressurizing time obtained through the prediction model with the bonding strength value manually set in advance, marking the sample pieces higher than the predicted value as triangles, and marking the sample pieces lower than the predicted value as circles, and carrying out internal verification and optimization on the prediction model.
Step five, verifying and optimizing the prediction model established in the step four: designing a set of pressurizing time t3The ceramic matrix composite material bonding test sample piece is detected by terahertz waves, different waveform characteristic values are extracted, and characteristic value imaging is carried out. Carrying out a bonding strength test on the ceramic matrix composite bonding test sample piece subjected to terahertz wave detection to obtain bonding strength test data of pressurization time t3, and bonding the bonding strengthThe degree test data is imported into a prediction model established by a support vector machine to obtain a predicted bonding strength value, the predicted bonding strength value is compared with an actual bonding strength value, if the predicted value is closer to the actual bonding strength value, the prediction is considered to be accurate, and the prediction model is established to be correct; and if the difference between the predicted numerical value and the actual bonding strength numerical value is larger, the prediction model is considered to be established wrongly, and the prediction model is reestablished. And repeating the steps until a predicted value close to the predicted value is obtained, optimizing the prediction model, and finally obtaining a conclusion, wherein the optimized prediction model is used for classifying and optimizing the pressurization parameters of the ceramic matrix composite material bonding process.
Claims (6)
1. A multilayer structure bonding pressurization parameter terahertz detection optimization method is characterized by comprising the following steps:
step one, designing and manufacturing two groups of different pressurizing time t1、t2The test piece is bonded with the multilayer structure, the waveform characteristic value of the test piece is extracted, terahertz detection is respectively carried out on the two groups of test pieces, terahertz time-domain spectral imaging is carried out on detection data, and a terahertz time-domain atlas of the test piece is obtained;
step two, carrying out multi-eigenvalue imaging on the waveform data obtained by the terahertz detection in the step one to obtain eigenvalues of different imaging modes;
thirdly, performing bonding strength tests on the test pieces subjected to terahertz detection and different pressurization times, and comparing the actual tensile strength of the adhesive layers of the test pieces subjected to terahertz detection and different pressurization times under the condition of different pressurization times;
step four, establishing a prediction model by using a support vector machine: inputting the characteristic values of different imaging modes obtained in the step two and the bonding strength test data of the test pieces with different pressurizing times obtained in the step three into a support vector machine, and establishing a prediction model;
step five, verifying and optimizing the prediction model established in the step four: additionally, a set of pressurization times t is designed and manufactured3The terahertz detection is utilized to carry out multi-eigenvalue imaging, the eigenvalues of different imaging modes are extracted and input into the test sample piece established in the step fourPredicting the model to obtain a bonding strength prediction result; for the pressurizing time t3Carrying out a bonding strength test on the test sample to obtain a test result, judging the consistency of a prediction result obtained by the prediction model and an actual bonding strength result by using the test result, and optimizing the prediction model; the optimized prediction model is used for classifying and optimizing the pressurization parameters of the ceramic matrix composite material bonding process.
2. The method for terahertz detection and optimization of multilayer structure bonding pressurization parameters as claimed in claim 1, wherein the multilayer structure bonding test piece is a ceramic matrix composite bonding test piece which sequentially comprises a ceramic-based material, a first organic silica gel layer, a buffer pad, a second organic silica gel layer and a bonding substrate from top to bottom.
3. The terahertz detection and optimization method for the bonding pressurization parameters of the multilayer structure as claimed in claim 2, wherein in the first step, the following waveform characteristic values are respectively extracted for test pieces with different pressurization times: the energy, the variation coefficient, the variance and the flight time of the first organic silica gel layer, the energy, the kurtosis, the variance and the flight time of the second organic silica gel layer, the energy and the variance of the buffer cushion and the flight time from the first organic silica gel layer to the second organic silica gel layer.
4. The multilayer structure bonding pressurization parameter terahertz detection optimization method according to claim 2, wherein in the second step, different imaging modes are adopted for different areas of the test piece to obtain characteristic values of the different imaging modes: performing power spectrum imaging and variance imaging on the first organic silica gel layer, the second organic silica gel layer and the buffer pad; independently carrying out coefficient of variation imaging on the first organic silica gel layer; the second organic silica gel layer is subjected to kurtosis imaging; and performing flight time imaging on the upper surface of the first organic silicon adhesive layer, the lower surface of the second organic silicon adhesive layer, and the range from the upper surface of the first organic silicon adhesive layer to the lower surface of the second organic silicon adhesive layer.
5. The multilayer structure bonding pressurization parameter terahertz detection optimization method according to claim 1, wherein the prediction model established in the fourth step is as follows:
H1:(ω,x)+b＝-1
H0:(ω,x)+b＝0
H2:(ω,x)+b＝1
yi((ω,xi)+b)≥1
from the above classification criteria, a classification decision function can be derived:
f(x)＝sgn((ω*,x)+b*)
where f (x) is the result of model prediction, let ω be*、b*Is the optimal solution of the model.
6. The multilayer structure bonding pressurization parameter terahertz detection optimization method according to claim 1, wherein the fourth step further comprises internal verification of a prediction model: different pressurizing time t obtained from the step one1，t2Inputting the characteristic value of the test piece into a prediction model established by a support vector machine to obtain the predicted bonding strength values of the test piece at different pressurizing time; and comparing the predicted bonding strength value with a bonding strength value manually set in advance, and carrying out internal verification and optimization on the prediction model.
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