CN116908304B - Polycrystalline material grain size assessment method based on ultrasonic wake average power attenuation - Google Patents
Polycrystalline material grain size assessment method based on ultrasonic wake average power attenuation Download PDFInfo
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Abstract
The invention provides a polycrystalline material grain size assessment method based on ultrasonic wake average power attenuation, which comprises the following steps: preparing a plurality of samples with the same thickness as the polycrystalline material to be detected, determining the mode and parameters of an excitation signal, and constructing a polycrystalline material average grain size detection system; respectively exciting and receiving ultrasonic signals of samples with different average grain sizes, and carrying out band-pass filtering pretreatment; selecting signals of a wake wave time window and a reference time window, respectively extracting average power, and calculating wake wave average power attenuation according to a time difference; establishing a logarithmic linear model between the average grain size of the sample and the average power attenuation of the wake wave based on the equivalent attenuation coefficient model; and collecting signals of the polycrystalline material with unknown grain size to be detected, filtering, extracting corresponding wake wave average power attenuation, and determining the average grain size of the polycrystalline material according to the logarithmic linear model. The invention has the advantages of high detection efficiency, relatively easy operation, strong practicability and accurate detection result.
Description
Technical Field
The invention belongs to the technical field of nondestructive testing of grain sizes of polycrystalline materials, and particularly relates to a polycrystalline material grain size assessment method based on ultrasonic wake average power attenuation.
Background
Nickel-base superalloys are widely used in certain advanced aircraft engines as a representative key material for manufacturing core components such as turbine disks of modern advanced aircraft engines. Because of the limitation of the processing technology, the large-specification high-temperature alloy blank for the turbine disk is easy to generate the phenomenon of uneven grain structure of the material in the processing process, partial grains are coarse, the tensile strength and fatigue resistance performance are reduced, the mechanical performance dispersibility is high, the product is invalid and scrapped when serious, and the integrity and reliability of equipment are seriously affected. Therefore, the development of the high-efficiency and accurate alloy blank grain size assessment method is beneficial to realizing the stable quality of the high-temperature alloy turbine disc under the condition of mass production, and has important engineering practical value.
Currently, the grain size detection methods commonly used are mainly divided into two types: destructive testing methods and non-destructive testing methods. Destructive inspection techniques such as optical metallography, electron back scattering diffraction methods evaluate the average grain size by cutting small test pieces from a sample to be inspected, and then photographing the grain structure characteristics of the surface of the test pieces by an optical or electron microscope. Although the destructive testing method can be used for characterizing the microstructure of the crystal grain with high precision, the cut sample is often not allowed for the actual structure to be tested of engineering, and the testing cost is high, so that the application range of the method is limited. Therefore, nondestructive testing techniques such as X-ray testing methods, bulk wave ultrasonic testing methods, and the like have been highly developed. The X-ray detection method utilizes the characteristic that X-rays interact with substances to generate attenuation or scattering when the X-rays propagate in an object to be detected to observe the microstructure of grains in the object, but the equipment is expensive, long-time measurement is needed, and the efficiency is low. Bulk wave ultrasound based detection methods evaluate material properties by generating ultrasound waves in a material and analyzing characteristics of the waves after interaction therein with internal microstructures of the material. The selected bulk wave ultrasonic waveform characteristics mainly comprise wave velocity characteristics, backscattering characteristics and attenuation characteristics. The principle of wave velocity characteristic is that the sound velocity is calculated according to the thickness of the measured material and the time delay time between two echoes, and then the average grain size is estimated, but the relation between the wave velocity and the grain size is non-monotonic under a certain grain size range, and the sensitivity to partial metal materials is weaker, and an accurate measuring system is needed for detection. The backscattering characteristic focuses on a large amount of scattered waves generated by the action of ultrasonic waves on grain boundaries mismatched with internal acoustic impedance in a material, and the scattered waves are similar to noise disturbance signals between a main wave signal and an echo signal on a time domain signal, but the backscattering signals need to be extracted through multiple spatial sampling averages, so that the efficiency is low. The attenuation characteristic means that the energy carried by the ultrasonic waves can be weakened along with the increase of the propagation distance in the propagation process of the inside of the material, the surface echo and the progressive decline of the amplitude of each bottom wave in the pulse echo method can be observed based on the attenuation characteristic to evaluate the grain size, and the stability is better, but due to the limitation of bulk wave ultrasonic waves, only the average grain size of a single path can be detected, the space densely distributed point sampling is needed for a large-sized structure, the efficiency is lower, and the abnormal omission of local grains can be caused.
The wake is formed by multiple scattering of the ultrasonic waves in a non-uniform medium, and appears as a tail directly behind the wave. Since the wake has a resampling effect on space over a longer time scale than the bulk wave, it has a higher sensitivity to microscopic grain structure features and a wide range of grain size assessment can be achieved by a single measurement at a single sensor location. Therefore, in combination with the advantages of ultrasonic attenuation characteristics and wake detection, it is urgent and extremely important to find a polycrystalline material grain size assessment method based on ultrasonic wake average power attenuation to efficiently achieve average grain size assessment.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a polycrystalline material grain size assessment method based on ultrasonic wake average power attenuation. Firstly, determining the mode and parameters of an excitation signal by preparing a plurality of samples with the same thickness as a polycrystalline material to be detected, and then constructing a polycrystalline material average grain size detection system; respectively exciting and receiving ultrasonic signals of samples with different average grain sizes, and carrying out band-pass filtering pretreatment; selecting signals of a wake wave time window and a reference time window, respectively extracting average power, and calculating wake wave average power attenuation according to a time difference; establishing a logarithmic linear model between the average grain size of the sample and the average power attenuation of the wake wave based on the equivalent attenuation coefficient model; and collecting signals of the polycrystalline material with unknown grain size to be detected, filtering, extracting corresponding wake wave average power attenuation, and determining the average grain size of the polycrystalline material according to the logarithmic linear model. Compared with the traditional bulk wave method, the method has the advantages of high detection efficiency, relatively easy detection, strong practicability and more accurate detection result.
Specifically, the invention provides a polycrystalline material grain size assessment method based on ultrasonic wake average power attenuation, which comprises the following steps:
s1, preparing a plurality of samples with the same thickness as a polycrystalline material to be detected, determining a mode and parameters of an excitation signal, and constructing a polycrystalline material average grain size detection system;
s2, respectively exciting and receiving ultrasonic signals of samples with different average grain sizes, and carrying out band-pass filtering pretreatment on the collected ultrasonic signals;
s3, selecting a wake time window [ T, t+T ]]Reference time window [ T ', T' +T ] 0 ]Respectively extracting average powers, and calculating wake average power attenuation according to the time difference of the average powers, wherein the method comprises the following sub-steps:
s31, extracting signal voltage amplitude values V (tau) of different moments tau in a selected wake time window [ T, t+T ] according to a time window starting point T and a time window width T, and calculating wake average power P (T) at the moment T:
the wake average power P (t) represents the energy state of the wake part of the ultrasonic signal at the moment t;
s32, according to the starting point T' of the time window and the width T of the time window 0 Extracting selected wake time windows]t',t'+T 0 ]The signal voltage amplitude V (tau) at different moments tau within a selected reference time window T ', T' +T 0 ]In, calculate t 0 ＝t'+T 0 The reference average power at time/2 is P (t 0 )：
Reference average power P (t) 0 ) Representing ultrasoundThe main signal part is at t 0 The energy state at the moment;
s33, according to the ultrasonic signals, at t and t 0 Wake average power at time P (t) and reference average power P (t) 0 ) Time difference Δt=t-t 0 Calculating wake average power attenuationThe method comprises the following steps:
wake average power decayWake power dissipation, which represents the separation Δt due to grain scattering and other factors;
s4, establishing the average grain size d of the sample and the average power attenuation of the wake wave based on an equivalent attenuation coefficient modelLog linear model between:
wherein θ 1 、θ 2 Respectively representing a first fitting parameter of the model and a second fitting parameter of the model, and evaluating by a least square method;
s5, collecting ultrasonic signals of the polycrystalline material with unknown grain size to be detected by utilizing a polycrystalline material average grain size detection system, filtering, and extracting wake average power attenuation of the polycrystalline material according to the step S3And determining the average grain size of the polycrystalline material based on the log-linear model described in step S4>The calculation method comprises the following steps:
preferably, the step S1 specifically includes the following substeps:
s11, preparing a plurality of samples with the same chemical composition and different average grain sizes according to the material composition information of the polycrystalline material to be detected, and cutting the samples into plate-shaped samples with the thickness consistent with that of the polycrystalline material to be detected;
s12, an excitation source for exciting an ultrasonic signal is a Hanning window modulated sine wave pulse with a single center frequency, and the ultrasonic signal frequency is selected according to the elastic property and thickness information of the polycrystalline material to be detected;
s13, outputting ultrasonic waves by using a single channel of an arbitrary function generator, and forming two output ends through a three-way connector, wherein the first output end is connected to a high-voltage power amplifier, amplified and then transmitted to an excitation ultrasonic transducer chip, and the second output end is directly transmitted to a trigger channel of a mixed domain oscilloscope;
s14, for each sample, a pair of excitation transducer wafers and receiving transducer wafers with the same center frequency are closely placed and fixed at the center position of the sample surface;
s15, after each excitation, according to the trigger signal received by the trigger channel, the ultrasonic signal transmitted in the sample is collected through the receiving transducer wafer and transmitted to the collecting channel of the mixed domain oscilloscope.
Preferably, the step S2 specifically includes the following substeps:
s21, randomly dicing and sampling each sample, and obtaining the actual grain size;
s22, detecting each sample in a pulse echo mode, exciting a transducer wafer to generate an excitation ultrasonic signal in the sample, and receiving the ultrasonic signal transmitted in the sample by the transducer wafer;
s23, repeatedly executing the step S21 and the step S22 until ultrasonic signals corresponding to the samples with different average grain sizes are obtained, and carrying out band-pass filtering pretreatment on the ultrasonic signals.
Preferably, the signal received by the receiving transducer wafer in step S2 is an average of 128 consecutive signal acquisitions by the mixed domain oscilloscope.
Preferably, in step S2, the ultrasonic signals in each sample are detected by pulse echo.
Preferably, the wake time window and the reference time window extracted in step S3 are located in the wake portion and the main portion of the ultrasonic signal, respectively.
Preferably, the selection of the wake sector is initiated with twice the arrival time of the shortest boundary echo, and the wake signals within the selected wake time window have a high signal-to-noise ratio.
Preferably, the average grain size d of the sample in step S21 is quantified by means of electron back-scattering diffraction technique.
Preferably, the model first fitting parameter and the model second fitting parameter in step S4 are evaluated by a least squares method.
Preferably, the system for detecting the average grain size of the polycrystalline material in the step S1 comprises an arbitrary function generator, a tee joint, a high-voltage power amplifier, an excitation transducer wafer, a receiving transducer wafer, a mixed domain oscilloscope and a superior controller;
the three-way connector is provided with two output ends, the first output end is connected to a high-voltage power amplifier, amplified and then transmitted to an excitation ultrasonic transducer wafer, and the second output end is directly transmitted to a trigger channel of the mixed domain oscilloscope; the exciting transducer chip is used for generating an exciting ultrasonic signal, and the receiving transducer is used for receiving the transmitted ultrasonic signal and transmitting the ultrasonic signal to the upper controller.
Compared with the prior art, the invention has the beneficial technical effects that:
(1) The invention provides a polycrystalline material grain size assessment method based on ultrasonic wake average power attenuation, which uses superwavesThe acoustic scattering theory is used as a theoretical basis, the influence of microstructure in the polycrystalline material on the ultrasonic property is utilized, the wake wave time window and the reference time window are intercepted respectively, wake wave average power attenuation corresponding to the average grain size of the material is extracted, and the average grain size d of the sample and the wake wave average power attenuation are constructedThe method comprises the steps of obtaining a log linear model, then obtaining wake average power attenuation of the polycrystalline material to be detected, and determining the average grain size of the polycrystalline material to be detected according to the log linear model; the method can accurately obtain the average grain size of the polycrystalline material without damaging the polycrystalline material to be detected, combines the advantages of ultrasonic attenuation characteristics and wake wave detection, and provides a method for quantitatively evaluating the average grain size of the polycrystalline material by utilizing wake wave average power attenuation, and the method has high detection efficiency and good measurement robustness.
(2) Compared with the traditional ultrasonic method adopting direct wave attenuation, the polycrystalline material grain size assessment method based on ultrasonic wake wave average power attenuation provided by the invention adopts the attenuation of the wake wave part for assessing ultrasonic waves and materials, can assess large-range grain size information through single measurement at a single sensor position, and has higher detection efficiency; the average power is used for describing the energy state of ultrasonic waves, so that the defects that the ultrasonic amplitude after being propagated in a highly non-uniform structure is difficult to extract and easy to interfere are avoided, more robust grain size assessment is realized, the actual grain size measured by an electronic back scattering diffraction technology is contained in a 99% confidence interval of the obtained grain size prediction, and the reliability of prediction is illustrated.
Drawings
Other features, objects and advantages of the present application will become more apparent from the detailed description of non-limiting embodiments, which proceeds with reference to the accompanying drawings.
FIG. 1 is a flow chart of a polycrystalline material grain size assessment method based on ultrasonic wake average power attenuation in accordance with the present invention;
FIG. 2 is a schematic diagram of a system and a process for detecting the average grain size of a polycrystalline material according to an embodiment of the present disclosure;
FIG. 3 is a schematic diagram of sample size and transducer placement in an embodiment of the invention;
FIG. 4 is a graph showing the comparison of filtered waveforms in a reference time window and a wake time window for different samples according to an embodiment of the present invention;
FIG. 5 is a log-linear relationship between the average grain size of a sample and the average power decay of the wake, as compared to experimental values for the sample and polycrystalline material to be tested, using a log-linear fit, in an embodiment of the present invention.
Detailed Description
The present application is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be noted that, for convenience of description, only the portions related to the present invention are shown in the drawings.
It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be combined with each other. The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
The invention provides a polycrystalline material grain size assessment method based on ultrasonic wake average power attenuation, which is shown in fig. 1 and comprises the following steps:
s1, preparing a plurality of samples with the same thickness as the polycrystalline material to be detected, determining the mode and parameters of an excitation signal, and constructing a polycrystalline material average grain size detection system.
The polycrystalline material average grain size detection system comprises an arbitrary function generator, a BNC three-way connector, a high-voltage power amplifier, an excitation transducer wafer, a receiving transducer wafer, a mixed domain oscilloscope and a superior controller. The method comprises the steps that ultrasonic waves are output by a single channel of an arbitrary function generator and are transmitted to a three-way connector, the three-way connector is provided with two output ends, the first output end is connected to a high-voltage power amplifier, the amplified ultrasonic waves are transmitted to an excitation ultrasonic transducer chip, and the second output end is directly transmitted to a trigger channel of a mixed domain oscilloscope; the exciting transducer chip is used for generating an exciting ultrasonic signal, and the receiving transducer is used for receiving the transmitted ultrasonic signal and transmitting the ultrasonic signal to the upper controller. In this embodiment, the upper controller is a computer.
The method specifically comprises the following substeps:
s11, preparing a plurality of samples with the same chemical components and different average grain sizes according to the material composition information of the polycrystalline material to be detected (also called as polycrystalline metal material or polycrystalline metal structure), and cutting the samples into plate-shaped samples with the same thickness as the structure to be detected.
S12, an excitation source for exciting ultrasonic signals is a Hanning window modulated sine wave pulse with a single center frequency, and the ultrasonic signal frequency is selected to balance the sensitivity and resolution of grain size measurement according to the pre-known elastic property and thickness information of the polycrystalline material to be detected.
S13, outputting ultrasonic waves by utilizing a single channel of an arbitrary function generator, and forming two output ends through BNC three-way connectors, wherein the first output end is connected to a high-voltage power amplifier, amplified and then transmitted to an excitation ultrasonic transducer wafer, and the second output end is directly transmitted to a trigger channel of a mixed domain oscilloscope.
S14, for each sample, preparing a pair of excitation transducer wafers and receiving transducer wafers with the same center frequency, placing the two wafers in close contact, and fixing the wafers at the center of the surface of the sample through glue.
S15, after each excitation, according to the trigger signal received by the trigger channel, the ultrasonic signal transmitted in the sample is collected through the receiving transducer wafer and transmitted to the collecting channel of the mixed domain oscilloscope for subsequent data processing.
S2, respectively exciting and receiving ultrasonic signals of samples with different average grain sizes, and carrying out band-pass filtering pretreatment on the collected ultrasonic signals. The method specifically comprises the following substeps:
s21, randomly dicing and sampling each sample, and quantifying the real grain size by adopting an electron back scattering diffraction technology as a reference.
S22, detecting each sample by adopting a pulse echo mode, exciting the transducer wafer to generate an exciting ultrasonic signal in the sample, and receiving the ultrasonic signal transmitted in the sample by the receiving transducer. The ultrasonic signal received by the receiving transducer is the average value of 128 continuous signal acquisitions of the mixed domain oscilloscope.
S23, repeatedly executing the step S21 and the step S22 until ultrasonic signals corresponding to samples with different average grain sizes are obtained, and carrying out band-pass filtering pretreatment on the ultrasonic signals to remove frequency components irrelevant to grain size evaluation.
S3, selecting a wake time window [ T, t+T ]]Reference time window [ T ', T' +T ] 0 ]Respectively extracting average power of the two signals, and calculating wake average power attenuation according to the time difference. The method specifically comprises the following substeps:
s31, extracting signal voltage amplitude values V (tau) of different moments tau in a selected wake time window [ T, t+T ] according to a time window starting point T and a time window width T, and calculating wake average power P (T) at the moment T as follows:
the wake average power P (t) represents the energy state of the wake portion of the ultrasonic signal at time t. The selection of the wake-up section is preferably twice the arrival time of the shortest boundary echo as the starting moment and the wake-up signal within the selected time window must have a higher signal-to-noise ratio.
S32, according to the starting point T' of the time window and the width T of the time window 0 Extracting a selected wake time window [ T ', T' +T ] 0 ]The signal voltage amplitude V (tau) at different moments tau within a selected reference time window T ', T' +T 0 ]In, calculate t 0 ＝t'+T 0 The reference average power at time/2 is P (t 0 )：
Reference average power P (t) 0 ) Representing the main wave part of the ultrasonic signal at t 0 Energy state at time.
S33, according to the ultrasonic signals, at t and t 0 Wake average power at time P (t) and reference average power P (t) 0 ) Time difference Δt=t-t 0 Calculating wake average power attenuationThe method comprises the following steps:
wake average power decayRepresenting wake power dissipation at the separation Δt due to grain scattering and other factors.
S4, establishing the average grain size d of the sample and the average power attenuation of the wake wave based on an equivalent attenuation coefficient modelLog linear model between:
wherein θ 1 、θ 2 Representing model first fitting parameters and model second fitting parameters, respectively, both of which are evaluated by the least squares method.
S5, collecting ultrasonic signals of the polycrystalline material with the unknown grain size to be detected, and extracting wake average power attenuation of the polycrystalline material with the unknown grain size to be detected according to the step S3And determining the average grain size of the polycrystalline material according to the log-linear model described in S4>The method comprises the following steps:
the present invention will be described in further detail with reference to a specific case of detecting grain size of polycrystalline material, and the overall flow is shown in fig. 2.
S1, preparing five samples (respectively #1, #2, #3, #4 and # 5) with the same components as a nickel-based superalloy sample to be detected with the brand GH742, wherein the thicknesses of the sample and the sample to be detected are 5mm, the length is 190mm, and the width is 100mm, and the specific process is as follows:
s11, the solution temperature and the solution time adopted by the five samples in the heat treatment link are different, so that the five samples have different average grain sizes.
S12, the excitation signal is a 3.5-period Hanning window modulation sine pulse with the center frequency of 5 MHz.
S13, generating an excitation signal by using an arbitrary function generator (Tektronix, AFG 31022) to form two paths of output through a BNC three-way connector, wherein a first output end is connected to a high-voltage power amplifier (Aigtek, ATA-4012), the peak-to-peak value is 80V after amplification, the amplified peak-to-peak value is transmitted to an excitation transducer wafer (Siansonic, the center frequency is 5 MHz), and a second output end is directly transmitted to a trigger channel of a mixed domain oscilloscope (Tektronix, MDO 3104).
S14, a circular excitation transducer wafer with the diameter of 10mm and a receiving transducer wafer (Siansonic, center frequency of 5 MHz) are closely placed, and are fixed at the center position of the surface of the sample through 502 quick-drying glue, as shown in figure 3.
S15, after each excitation, according to the trigger signal received by the trigger channel, the ultrasonic signal transmitted in the sample is collected through the receiving transducer wafer and transmitted to the collecting channel of the mixed domain oscilloscope.
S2, randomly cutting and sampling each sample, taking a sample microstructure photo by adopting an electron back scattering diffraction technology, obtaining a lognormal distribution experimental value of the grain size by an equivalent diameter method, and taking a lognormal distribution mean value of the grain size as a reference of the real grain size. Respectively exciting and receiving ultrasonic signals of samples with different average grain sizes, carrying out time domain averaging on the acquired signals for 128 times in advance in a mixed domain oscilloscope, inputting the acquired signals to an upper controller, and then carrying out band-pass filtering pretreatment with the passband of [0.1MHz and 10MHz ] by adopting a wavelet filter.
S3, FIG. 4 shows the ultrasonic signals within the wake time windows [66.22 μs,77.22 μs ] and the reference time windows [0.5 μs,40 μs ] selected for five samples. The characteristic sensitive to grain size is the attenuation of the wake-up portion of the received signal data, while the different initial contact states ultimately change the energy transferred into the structure, which affects both the wake-up portion and the reference window portion of the signal. Since the attenuation coefficient is a relative value, the influence of the contact state on the signal can be minimized. In this case, the variation in attenuation coefficient between different samples is largely attributed to the different grain sizes. And respectively extracting average power from time windows of signals corresponding to the samples, and calculating wake average power attenuation according to a time difference according to a formula (3).
S4, as shown in a formula (4), establishing the average grain size d of the sample and the average power attenuation of the wake wave through logarithmic linear fitting of experimental resultsA log-linear relationship model of (a) is shown in figure 5. Wherein, the solid circle mark represents the relation between the reference average grain size obtained by quantifying each sample through the electron back scattering diffraction technique and the wake average power attenuation, the solid line draws the log linear least square fitting result, and the fitting obtains the first fitting parameter theta of the model 1 = 5.5343, model second fitting parameter θ 2 = 0.8732. As the average grain size increases, the wake average power decay increases in a log-linear trend.
S5, collecting signals of polycrystalline materials with unknown grain sizes to be detected, filtering, obtaining corresponding wake wave average power attenuation of the polycrystalline materials to be detected through the step S3, determining the average grain size of the polycrystalline materials according to a log linear model, and then evaluating the real average grain size by utilizing an electron back scattering diffraction technology for verification, wherein a verification result is shown as a solid square mark in FIG. 5. The results indicate that the true grain size measured by electron back-scattering diffraction techniques is included within the 99% confidence interval of the model drawn by the black dashed line, indicating that the proposed model can be used to evaluate the average grain size of the polycrystalline material to be measured, and that the predicted results are very accurate.
The invention provides a polycrystalline material grain size assessment method based on ultrasonic wake wave average power attenuation, which utilizes the influence of microstructure in polycrystalline material on ultrasonic properties, selects ultrasonic wake wave as a measurement signal, extracts wake wave average power attenuation corresponding to material average grain size by intercepting wake wave time window and reference time window respectively, and constructs sample average grain size d and wake wave average power attenuationThe log linear model is used for obtaining the wake wave average power attenuation of the polycrystalline material to be detected through experiments, so that the average grain size of the polycrystalline material can be detected efficiently; the detection method combines the advantages of ultrasonic attenuation characteristics and wake wave detection, can quantitatively evaluate the average grain size of the polycrystalline material in a wake wave average power attenuation mode, and has the advantages of high detection efficiency, good measurement robustness and accurate detection result.
Finally, what should be said is: the above embodiments are merely for illustrating the technical aspects of the present invention, and it should be understood by those skilled in the art that although the present invention has been described in detail with reference to the above embodiments: modifications and equivalents may be made thereto without departing from the spirit and scope of the invention, which is intended to be encompassed by the claims.
Claims (6)
1. A polycrystalline material grain size assessment method based on ultrasonic wake average power attenuation, which is characterized by comprising the following steps:
s1, preparing a plurality of samples with the same thickness as the polycrystalline material to be detected, determining the mode and parameters of an excitation signal, and constructing a polycrystalline material average grain size detection system;
the step S1 specifically comprises the following steps:
s11, preparing a plurality of samples with the same chemical composition and different average grain sizes according to the material composition information of the polycrystalline material to be detected, and cutting the samples into plate-shaped samples with the thickness consistent with that of the polycrystalline material to be detected;
s12, an excitation source for exciting an ultrasonic signal is a Hanning window modulated sine wave pulse with a single center frequency, and the ultrasonic signal frequency is selected according to the elastic property and thickness information of the polycrystalline material to be detected;
s13, outputting ultrasonic waves by using a single channel of an arbitrary function generator, and forming two output ends through a three-way connector, wherein the first output end is connected to a high-voltage power amplifier, amplified and then transmitted to an excitation ultrasonic transducer chip, and the second output end is directly transmitted to a trigger channel of a mixed domain oscilloscope;
s14, for each sample, a pair of excitation transducer wafers and receiving transducer wafers with the same center frequency are closely placed and fixed at the center position of the sample surface;
s15, after each excitation, according to the trigger signal received by the trigger channel, acquiring an ultrasonic signal transmitted in a sample through a receiving transducer wafer and transmitting the ultrasonic signal to an acquisition channel of the mixed domain oscilloscope;
s2, respectively exciting and receiving ultrasonic signals of samples with different average grain sizes, and carrying out band-pass filtering pretreatment on the collected ultrasonic signals;
s3, selecting a wake time window [ T, t+T ]]Reference time window [ T ', T' +T ] 0 ]Respectively extracting average power, and calculating wake average power attenuation according to time difference, wherein the method specifically comprises the following sub-steps;
s31, extracting signal voltage amplitude values V (tau) of different moments tau in a selected wake time window [ T, t+T ] according to a time window starting point T and a time window width T, and calculating wake average power P (T) at the moment T as follows:
wherein the wake average power P (t) represents the energy state of the wake part of the ultrasonic signal at the time t;
s32, according to the starting point T' of the time window and the width T of the time window 0 Extracting a selected reference time window [ T ', T' +T ] 0 ]The signal voltage amplitude V (tau) at different moments tau within a selected reference time window T ', T' +T 0 ]In, calculate t 0 ＝t′+T 0 Reference average power P (t) at time/2 0 ) The method comprises the following steps:
wherein the reference average power P (t 0 ) Representing the main wave part of the ultrasonic signal at t 0 The energy state at the moment;
s33, according to the ultrasonic signals, at t and t 0 Wake average power at time P (t) and reference average power P (t) 0 ) Time difference Δt=t-t 0 Constructing a wake average power equivalent attenuation coefficient model, and calculating wake average power attenuation of the sampleThe wake average power equivalent attenuation coefficient model is as follows:
the wake average power decayWake power dissipation, which represents the separation Δt;
the wake time window and the reference time window extracted in the step S3 are respectively positioned at the wake part and the main wave part of the ultrasonic signal; selecting the wake wave part with the arrival time of the double shortest boundary echo as the starting moment, wherein the wake wave signals in the selected wake wave time window have high signal-to-noise ratio;
s4, establishing the average grain size d of the sample and the average power attenuation of the wake wave based on the equivalent attenuation coefficient model of the average power of the wake waveThe log-linear model between is shown below:
wherein θ 1 、θ 2 Respectively representing a first fitting parameter of the model and a second fitting parameter of the model;
s4, evaluating a model first fitting parameter and a model second fitting parameter through a least square method;
s5, collecting ultrasonic signals of the polycrystalline material with unknown grain size to be detected by utilizing a polycrystalline material average grain size detection system, filtering, and extracting wake average power attenuation of the polycrystalline material according to the step S3And determining the average grain size of the polycrystalline material based on the log-linear model described in step S4>The calculation method comprises the following steps:
2. the method for evaluating grain size of polycrystalline material based on ultrasonic wake average power attenuation of claim 1, wherein step S2 specifically comprises the steps of:
s21, randomly dicing and sampling each sample, and obtaining the actual grain size;
s22, detecting each sample in a pulse echo mode, exciting a transducer wafer to generate an excitation ultrasonic signal in the sample, and receiving the ultrasonic signal transmitted in the sample by the transducer wafer;
s23, repeatedly executing the step S21 and the step S22 until ultrasonic signals corresponding to the samples with different average grain sizes are obtained, and carrying out band-pass filtering pretreatment on the ultrasonic signals.
3. The method of claim 1, wherein the signal received by the receiving transducer wafer in step S2 is an average of 128 consecutive signal acquisitions by a mixed domain oscilloscope.
4. The method for evaluating grain size of polycrystalline material based on average power attenuation of ultrasonic wake wave according to claim 1, wherein the ultrasonic signal in each sample is detected by pulse echo in step S2.
5. The method for evaluating grain size of polycrystalline material based on ultrasonic wake average power attenuation according to claim 1, wherein the actual grain size d of the sample in step S21 is quantified by means of electron back scattering diffraction technique.
6. The method for evaluating grain size of polycrystalline material based on ultrasonic wake average power attenuation of claim 1, wherein the system for detecting grain size of polycrystalline material in step S1 comprises an arbitrary function generator, a three-way connector, a high voltage power amplifier, an excitation transducer wafer, a receiving transducer wafer, a mixed domain oscilloscope, and a host controller;
the three-way connector is provided with two output ends, the first output end is connected to a high-voltage power amplifier, amplified and then transmitted to an excitation ultrasonic transducer wafer, and the second output end is directly transmitted to a trigger channel of the mixed domain oscilloscope; the exciting transducer chip is used for generating an exciting ultrasonic signal, and the receiving transducer is used for receiving the transmitted ultrasonic signal and transmitting the ultrasonic signal to the upper controller.
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