CN111220564B - Terahertz detection optimization method for bonding pressurization parameters of multilayer structure - Google Patents
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- VYPSYNLAJGMNEJ-UHFFFAOYSA-N Silicium dioxide Chemical compound O=[Si]=O VYPSYNLAJGMNEJ-UHFFFAOYSA-N 0.000 claims description 38
- 239000000741 silica gel Substances 0.000 claims description 38
- 229910002027 silica gel Inorganic materials 0.000 claims description 38
- 238000004364 calculation method Methods 0.000 claims description 22
- 239000012790 adhesive layer Substances 0.000 claims description 11
- XUIMIQQOPSSXEZ-UHFFFAOYSA-N Silicon Chemical compound [Si] XUIMIQQOPSSXEZ-UHFFFAOYSA-N 0.000 claims description 8
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Abstract
The invention discloses a terahertz detection optimization method for bonding pressurization parameters of a multilayer structure, which designs and manufactures two groups of different pressurization time t 1 、t 2 Bonding the test pieces by the multilayer structure, extracting waveform characteristic values of the test pieces, and respectively carrying out terahertz detection on the two groups of test pieces; carrying out multi-eigenvalue imaging and bonding strength tests 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 manufactured 3 The 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 aiming at pressurization time, and jointly optimize the bonding process parameters by combining the bonding test block bonding strength test experimental data.
The purpose of the invention is realized by the following technical scheme:
a multilayer structure bonding pressurization parameter terahertz detection optimization method is characterized in that a multilayer structure bonding test piece is a ceramic matrix composite bonding test piece, and comprises a ceramic base material, a first organic silica gel layer, a buffer cushion, a second organic silica gel layer and a bonding base body from top to bottom in sequence;
the multilayer structure bonding pressurization parameter terahertz detection optimization method comprises the following steps:
step one, designing and manufacturing two groups of different pressurizing time t 1 、t 2 The 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; in the first step, the following waveform characteristic values are respectively extracted from the test pieces with different pressurizing time: energy, variation coefficient, variance and flight time of the first organic silica gel layer, energy, kurtosis, variance and flight time of the second organic silica gel layer, energy and variance of the buffer pad, energy and variance of the first organic silica gel layer and energy, variance of the second organic silica gel layerFlight time of the silica gel layer;
step two, performing multi-eigenvalue imaging on the waveform data obtained by the terahertz detection in the step one to obtain eigenvalues of different imaging modes; in the second step, different modes are adopted for imaging different areas of the test piece to obtain characteristic values of 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; 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;
the power spectrum imaging calculation formula is as follows (1):
the terahertz time-domain waveform power spectrum imaging calculation interval is T, and the signal amplitude corresponding to the flight time T in the terahertz time-domain waveform is E (T);
the variance imaging calculation formula is as follows (2):
where N is the number of index sample points, E (t) i ) For the time of flight T in the terahertz time-domain waveform i A corresponding amplitude value;
and Avg is the average value of sampling points in the T interval. T is um Is the maximum value E of the amplitude in the interval T max (T) corresponding time-of-flight value, T um ＝{T i |max(E(T i )),T i ∈T up ω (t) is a Gaussian window function;
the calculation formula of the flight time imaging is as follows (3):
T t ＝T 2 -T 1 (3)
wherein, T 2 For the corresponding time of flight, T, of the lower surface of the imaging area 1 The flight time corresponding to the upper surface of the imaging area;
the kurtosis imaging calculation formula is as follows (4):
wherein, E (t) i ) As the time of flight T in a terahertz time-domain waveform i The corresponding amplitude. T is Cal Extracting a calculation interval with obvious change of waveform kurtosis characteristic value;
the calculation formula of the coefficient of variation is as follows (5):
C v ＝(σ(T)÷E(t))*100％ (5)
wherein, σ (T) is the standard deviation of the first organic silica gel layer waveform signal amplitude, and E (T) is the average value of the first organic silica gel layer waveform signal amplitude;
thirdly, performing bonding strength tests on the test pieces subjected to terahertz detection and having different pressurization times, and comparing the actual tensile strength of the adhesive layers of the test pieces having different pressurization times under the condition that the pressurization times are different;
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 piece with different pressurizing time 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 manufactured 3 The terahertz detection is utilized for carrying out multi-eigenvalue imaging on the test sample piece, eigenvalues of different imaging modes are extracted and input into the prediction model established in the step four, and a bonding strength prediction result is obtained; for the pressurization time t 3 Carrying out a bonding strength test on the test sample to obtain a test result, judging the consistency of the prediction result obtained by the prediction model and the actual bonding strength result by using the test result, and optimizing the prediction model; application of optimized prediction model to classification of pressurization parameters of ceramic matrix composite bonding processAnd optimizing.
Further, the prediction model established in the fourth step is as follows:
H 1 :(ω,x)+b＝-1
H 0 :(ω,x)+b＝0
H 2 :(ω,x)+b＝1
y i ((ω,x i )+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.
Further, the fourth step includes an internal verification of the prediction model: different pressurizing time t obtained from the step one 1 ，t 2 Inputting 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.
Drawings
FIG. 1 is a flow schematic diagram of a terahertz detection optimization method for a multilayer structure bonding pressurization parameter.
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 view showing the structure 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 firstlyDesigning and making two groups of comparison tests with different pressurizing time, extracting characteristic values of different imaging modes of a sample piece according to test data by combining terahertz waves, obtaining actual bonding strength data by combining a bonding strength test, establishing an optimization model by using the characteristic values of the different imaging modes of the two groups of test data, predicting a bonding strength result, and finally using a group of pressurizing time t as a set of pressurizing time 3 The 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 layered medium is labeled, in which (1) an interface is a tile-upper surface layered interface of a ceramic matrix composite bonding test piece, (2) a region is a first silicone rubber layer region, (3) a region is a buffer pad region, (4) a region is a second silicone rubber layer region, and (5) an interface is an interface where the 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: 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. The pressurizing time is respectively t 1 、t 2 After 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 of T, and terahertz waves at each detection point are obtainedThe amplitude E (t) of the waveform in the time domain. 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 pad up ，IM down ，IM dn Sum variance imaging V upσ ，V downσ ，V dnσ Separately performing coefficient of variation imaging on the first organic silica gel layer C v Performing kurtosis imaging K on the second organic silica gel layer, and performing flight time imaging T from 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 to the lower surface of the second organic silica gel layer tup ，T tdown ，T t Therefore, 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 sample points, E (t) i ) For the time of flight T in the terahertz time-domain waveform i The corresponding amplitude.
And Avg is the average value of sampling points in the T interval. T is um Is the maximum value E of the amplitude in the interval T max (T) corresponding time-of-flight value, T um ＝{T i |max(E(T i )),T i ∈T up And ω (t) is a Gaussian window function.
The calculation formula of the flight time imaging is as follows (3):
T t ＝T 2 -T 1 (3)
wherein, T 2 For the corresponding time of flight, T, of the lower surface of the imaging area 1 The 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 waveform i The corresponding amplitude. T is a unit of Cal The method is used for extracting a calculation interval with obvious change of waveform kurtosis characteristic values.
The calculation formula of the coefficient of variation is as follows (5):
C v ＝(σ(T)÷E(t))*100％ (5)
wherein, σ (T) is the standard deviation of the first organosilicone layer waveform signal amplitude, and E (T) is the average value of the first organosilicone layer waveform signal amplitude.
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 t 1 ，t 2 The 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:
H 1 :(ω,x)+b＝-1
H 0 :(ω,x)+b＝0
H 2 :(ω,x)+b＝1
y i ((ω,x i )+b)≥1
from the above classification criteria, a classification decision function (set ω) can be obtained * ，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 two 1 ，t 2 Inputting characteristic values of different imaging modes of the test piece into a support vector machine, establishing a prediction model by the support vector machine, and obtaining predicted bonding strength values of the test piece at different pressurizing time 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 t 3 The 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, introducing the bonding strength test data into a prediction model established by a support vector machine to obtain a predicted bonding strength value, comparing the predicted bonding strength value with an actual bonding strength value, and if the predicted value is closer to the actual bonding strength value, considering that the prediction is accurate and the prediction model is established without errors; if the predicted value is equal to the actual adhesionIf the intensity values have larger difference, 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 (3)
1. A terahertz detection optimization method for bonding pressurization parameters of a multilayer structure is characterized by comprising the following steps of,
the multilayer structure bonding pressurization parameter terahertz detection optimization method comprises the following steps:
designing and manufacturing two groups of multilayer structure bonding test pieces with different pressurizing times t1 and t2, extracting waveform characteristic values of the test pieces, respectively carrying out terahertz detection on the two groups of test pieces, and carrying out terahertz time-domain spectral imaging on detection data to obtain a terahertz time-domain atlas of the test pieces; in the first step, the following waveform characteristic values are respectively extracted from the test pieces with different pressurizing time: energy, variation coefficient, variance and flight time of the first organic silica gel layer, energy, kurtosis, variance and flight time of the second organic silica gel layer, energy and variance of the buffer cushion and flight time from the first organic silica gel layer to the second organic silica gel layer;
step two, performing multi-eigenvalue imaging on the waveform data obtained by the terahertz detection in the step one to obtain eigenvalues of different imaging modes; in the second step, different imaging modes are adopted for different areas of the test piece to obtain characteristic values of 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; 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; setting T as a terahertz time-domain waveform imaging calculation interval;
the power spectrum imaging calculation formula is as follows (1):
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 ) Is the time of flight t in the terahertz time-domain waveform i A corresponding amplitude value; t is a terahertz time-domain waveform imaging calculation interval; avg is the average value of the sampling points in the T interval; t is t um Is the maximum value E of the amplitude in the interval T max (t) corresponding time-of-flight value, t um ＝{t i |max(E(t i )),t i E, T, and omega (T) is a Gaussian window function;
the calculation formula of the flight time imaging is as follows (3):
T t ＝T 2 -T 1 (3)
wherein, T 2 For the corresponding time of flight, T, of the lower surface of the imaging area 1 The flight time corresponding to the upper surface of the imaging area;
the kurtosis imaging calculation formula is as follows (4):
wherein, E (t) i ) As the time of flight t in the terahertz time-domain waveform i A corresponding amplitude value; t is Cal Extracting a calculation interval with obvious change of waveform kurtosis characteristic values; avg down The average value of sampling points in the interval of the second organic silica gel layer is obtained;
the calculation formula of the coefficient of variation is as follows (5):
C v ＝(σ(T)÷E(T))*100％ (5)
wherein σ (T) is the standard deviation of the first organosilica gel layer waveform signal amplitude, and E (T) is the average value of the first organosilica gel layer waveform signal amplitude;
thirdly, performing bonding strength tests on the test pieces subjected to terahertz detection and having different pressurization times, and comparing the actual tensile strength of the adhesive layers of the test pieces having different pressurization times under the condition that the pressurization times are different;
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 piece with different pressurizing time 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: in addition, designing and manufacturing a group of test samples with the pressurization time t3, utilizing terahertz detection and carrying out multi-eigenvalue imaging, extracting eigenvalues of different imaging modes, inputting the eigenvalues into the prediction model established in the step four, and obtaining a bonding strength prediction result; carrying out a bonding strength test on the test sample piece with the pressurizing time t3 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;
the multilayer structure bonding test piece is a ceramic matrix composite bonding test piece which sequentially comprises a ceramic base material, a first organic silica gel layer, a cushion pad, a second organic silica gel layer and a bonding base body from top to bottom.
2. The multilayer structure bonding pressurization parameter terahertz detection optimization method according to claim 1, characterized in that the prediction model established in the fourth step is as follows:
H 1 :(ω,x)+b＝-1
H 0 :(ω,x)+b＝0
H 2 :(ω,x)+b＝1
y i ((ω,x i )+b)≥1
the classification decision function can be obtained from the above classification criteria:
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.
3. 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: inputting the characteristic values of the test pieces with different pressurizing times t1 and t2 obtained in the step one into a prediction model established by a support vector machine to obtain predicted bonding strength values of the test pieces with different pressurizing times; 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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