System for audibly detecting a precursor to material fracture of a sample under test

Abstract

A system for determining a trigger amplitude indicating an early sign of material fracture in a test sample includes a microphone that converts acoustic emissions emitted by the test sample into electrical signals. A load is applied to the test sample, and acoustic emissions are emitted when the load causes deformation of the test sample prior to material fracture. A control module communicates electrically with the microphone and executes instructions to monitor and filter the electrical signals generated by the microphone. The control module converts the electrical signals generated by the microphone into individual frequency components based on a Fast Fourier Transform (FFT). Each frequency component includes a peak intensity. The control module determines the trigger amplitude based on the peak intensities of the individual frequency components obtained from the FFT.

Description

Technical Field

[0001] This disclosure relates to a system and method for determining, based on audible sound, a triggering amplitude indicating an impending material fracture in a test sample. This disclosure also relates to a system for capturing images of a test sample using a camera, wherein the system, in response to determining that a triggering amplitude has occurred, instructs the camera to capture images at different frame rates. Background Technology

[0002] Image processing systems are becoming increasingly popular. However, image processing involves storing and processing relatively large amounts of data, which can require significant processing and storage resources. One factor affecting the amount of memory required to store large image files is the frame rate of the camera that captures the images. Frame rate is expressed as frames per second (fps) and represents the number of frames or images captured by the camera per second. Increasing the frame rate results in larger image files that require more memory, while decreasing the frame rate results in smaller image files that require less memory.

[0003] Therefore, while image processing systems achieve their intended purpose, there is a need in the art for a method to reduce the storage requirements of image processing systems. Summary of the Invention

[0004] According to several aspects, a system for determining a trigger amplitude indicating a precursor to material fracture in a test sample is disclosed. The system includes a microphone for converting acoustic emissions emitted by the test sample into electrical signals. A load is applied to the test sample, and acoustic emissions are emitted when the load causes the test sample to undergo deformation prior to material fracture. The system also includes a control module in electrical communication with the microphone, wherein the control module executes instructions to monitor the electrical signals generated by the microphone. The control module executes instructions to filter the electrical signals generated by the microphone to allow frequencies within a range of interest and attenuate frequencies outside the range of interest. The control module converts the electrical signals generated by the microphone into individual frequency components based on a Fast Fourier Transform (FFT), wherein each frequency component includes a peak intensity representing audible sound. The control module determines the trigger amplitude based on the peak intensities of the individual frequency components of the FFT.

[0005] In one respect, the individual frequency components of the FFT define the amplitude trajectory.

[0006] On the other hand, the control module determines the trigger amplitude by analyzing the amplitude trajectory using a kernel density estimation (KDE) function, where the KDE function creates a smoothed estimate of the amplitude trajectory, and where the smoothed estimate of the amplitude trajectory includes multiple peaks, each peak representing the number of times the peak amplitude appears in the FFT.

[0007] On the other hand, the control module determines the trigger amplitude by identifying the highest peak value of the smoothed estimate of the amplitude trajectory, where the highest peak value represents the peak amplitude that occurs most frequently in the FFT.

[0008] On the other hand, the highest peak value represents the audible sound produced by background noise.

[0009] On the other hand, the control module determines the trigger amplitude by identifying the second highest peak of the smoothed estimate of the amplitude trajectory, where the second highest peak represents the peak amplitude that appears most frequently in the FFT after the highest peak.

[0010] In one respect, the second highest peak value represents the acoustic emission emitted by the tested sample as it undergoes deformation before the material fractures.

[0011] On the other hand, the control module determines the trigger amplitude by identifying the valley between the highest peak and the second highest peak and selecting the amplitude value corresponding to the valley as the trigger amplitude.

[0012] On the other hand, frequencies outside the range of interest represent background noise.

[0013] In one aspect, a method is disclosed for determining a trigger amplitude indicating a precursor to material fracture in a test sample. The method includes converting acoustic emissions emitted by the test sample via a microphone into an electrical signal. A load is applied to the test sample, and acoustic emissions are emitted when the load causes the test sample to undergo deformation prior to material fracture. The method includes monitoring the electrical signal generated by the microphone by a control module. The method also includes filtering the electrical signal generated by the microphone to allow frequencies within a range of interest and attenuate frequencies outside the range of interest. The method further includes converting the electrical signal generated by the microphone into individual frequency components based on a Fast Fourier Transform (FFT), wherein each frequency component includes a peak intensity representing audible sound. Finally, the method includes determining the trigger amplitude based on the peak intensities of the individual frequency components of the FFT.

[0014] In one aspect, the individual frequency components of the FFT define the amplitude trajectory, and the method further includes analyzing the amplitude trajectory using a KDE function. The KDE function creates a smoothed estimate of the amplitude trajectory, and the smoothed estimate of the amplitude trajectory includes multiple peaks, each peak representing the number of times the peak amplitude appears in the FFT.

[0015] On the other hand, the method further includes identifying the highest peak of the smoothed estimate of the amplitude trajectory, where the highest peak represents the peak amplitude that occurs most frequently in the FFT.

[0016] In another aspect, the method further includes identifying a second peak in the smoothed estimate of the amplitude trajectory, where the second peak represents the peak amplitude that appears most frequently in the FFT after the peak.

[0017] In another aspect, the method further includes determining the trough between the highest peak and the second highest peak.

[0018] In one aspect, the method further includes selecting an amplitude value corresponding to the valley value as the trigger amplitude.

[0019] On the other hand, frequencies outside the range of interest represent background noise.

[0020] In one aspect, a system for capturing images is disclosed. The system includes a test sample, wherein a load is applied to the test sample causing it to deform prior to material fracture. The system also includes a camera for capturing images of the test sample, wherein the camera captures images at a first frame rate and a second frame rate, the first frame rate being less than the second frame rate. The system further includes a microphone that converts acoustic emissions emitted by the test sample into electrical signals, wherein the load is applied to the test sample, and the acoustic emissions are emitted when the load causes the test sample to deform prior to material fracture. Finally, the system includes a control module in electrical communication with the microphone and the camera. The control module executes instructions to monitor the camera capturing images at the first frame rate. The control module executes instructions to monitor the trigger amplitude of the electrical signal generated by the microphone, wherein the electrical signal generated by the microphone indicates an acoustic amplitude. The control module executes instructions to determine that the electrical signal generated by the microphone indicates that a trigger amplitude has occurred. In response to determining that a trigger amplitude has occurred, the control module instructs the camera to capture images of the test sample at a second frame rate, wherein the trigger amplitude indicates a precursor to material fracture in the test sample.

[0021] In one aspect, the tested sample undergoes one of the following tests: open-hole tensile test, ultimate tensile strength test, notched tensile test, compression test, and torsion test.

[0022] On the other hand, the tested sample is composed of at least one of the following: glass fiber composite material, carbon fiber composite material, basalt fiber composite material, plastic, filled plastic and fiber reinforced polymer.

[0023] On the other hand, the first frame rate is about five frames per second (fps), and the second frame rate is about fifty fps.

[0024] Further areas of application will become apparent from the description provided herein. It should be understood that the descriptions and specific examples are for illustrative purposes only and are not intended to limit the scope of this disclosure. Attached Figure Description

[0025] The accompanying drawings described herein are for illustrative purposes only and are not intended to limit the scope of this disclosure in any way.

[0026] Figure 1 This is a schematic diagram of a system for determining precursors of material fracture in a test sample according to an exemplary embodiment, wherein the system includes a control module in electronic communication with a microphone;

[0027] Figure 2 The illustration shows an embodiment of the invention. Figure 1 The block diagram of the control module shown;

[0028] Figure 3 The illustration shows a process from an exemplary embodiment. Figure 1 An exemplary Fast Fourier Transform (FFT) graph of the electrical signal received by the microphone is shown.

[0029] Figure 4 The diagram illustrates the curves used to determine the kernel density estimation (KDE) function according to an exemplary embodiment. Figure 3 The smoothed estimate of the FFT shown;

[0030] Figure 5 The illustration shows the use according to an exemplary embodiment. Figure 1 The flowchart shown illustrates the process of determining the trigger amplitude using the system; and

[0031] Figure 6 This is a schematic diagram of a system for capturing images of a sample under test using a camera, according to an exemplary embodiment. Detailed Implementation

[0032] The following description is exemplary in nature and is not intended to limit this disclosure, application, or use.

[0033] refer to Figure 1 The illustration depicts an exemplary system 10 for determining precursors of material fracture in a test sample 12. In the non-limiting embodiment shown, system 10 includes a test sample 12, a fixing device 14 for securing the test sample 12, a microphone 20, and a control module 22. In the example shown, the test sample 12 is a tensile test sample comprising two enlarged ends 30 and a metering section 32 located between the two enlarged ends 30; however, the test sample 12 may also include other components. In the embodiment, the test sample 12 is composed of at least one of glass fiber composite, carbon fiber composite, basalt fiber composite, plastic, filled plastic, or any other fiber-reinforced polymer (such as carbon fiber reinforced polymer (CFRP)); however, it should be understood that other types of materials may also be used.

[0034] Figure 1The diagram illustrates a fixing device 14 comprising two gripping devices 34 that clamp and secure the enlarged end 30 of the sample 12 being tested. However, it should be understood that... Figure 1 This is merely an example, and other types of clamps may also be used. The clamping device 14 applies a load 38 to the sample 12 being tested. For example, Figure 1 The load 38 is illustrated as the tensile force applied to the test sample 12. In one embodiment, the test sample 12 undergoes an open-hole tensile test, an ultimate tensile strength test, a notched tensile test, a compression test, or a torsion test; however, it should be understood that other types of tests, such as material fracture tests, may also be used to test the test sample 12. It should be understood that in some embodiments, the test sample 12 may experience invisible material fracture.

[0035] Before the material fractures, the test sample 12 emits acoustic emissions. Specifically, acoustic emissions are emitted when the test sample 12 undergoes deformation before the load 38 causes it to fracture under the load 38. The microphone 20 converts the acoustic emissions emitted by the test sample 12 into an electrical signal 40 (see...). Figure 2 The electrical signal 40 generated by microphone 20 indicates the acoustic amplitude. Control module 22 communicates electronically with microphone 20 and monitors the electrical signal 40 generated by microphone 20. As described below, control module 22 determines the trigger amplitude of electrical signal 40, which indicates that the test sample 12 is about to break, caused by the load 38 applied by the fixing device 14. In other words, the trigger amplitude indicates a precursor to material fracture in the test sample 12.

[0036] Figure 2 This diagram illustrates a block diagram of control module 22, which includes a bandpass filter 50, a Fast Fourier Transform (FFT) module 52, a kernel density estimation (KDE) function module 54, and an analyzer module 56. Control module 22 can refer to electronic circuitry in a system-on-a-chip, combinational logic circuitry, a field-programmable gate array (FPGA), a processor (shared, dedicated, or grouped) executing code, or a combination thereof, or some or all of these. Additionally, control module 22 can be microprocessor-based, such as a computer having at least one processor, memory (RAM and / or ROM), and associated input and output buses. The processor can operate under the control of an operating system residing in memory. The operating system can manage computer resources so that computer program code embodied as one or more computer software applications (such as applications residing in memory) can have instructions executed by the processor. In alternative embodiments, the processor can directly execute the application, in which case the operating system can be omitted.

[0037] refer to Figure 1 and Figure 2The bandpass filter 50 of control module 22 can be implemented using analog components (such as resistors, inductors, and capacitors) or alternatively as a digital filter. The bandpass filter 50 of control module 22 filters the electrical signal 40 generated by microphone 20 (see...). Figure 1 Specifically, the bandpass filter 50 allows frequencies within the range of interest while attenuating frequencies outside the range of interest. The frequencies within the range of interest include acoustic emissions emitted by the test sample 12 and the sound emitted by the test sample 12 during material fracture. The frequencies outside the range of interest represent background noise that the microphone 20 can detect. It should be understood that the specific values ​​of the frequencies within the range of interest depend on variables such as the material of the test sample 12 and the specific type of test the test sample 12 undergoes.

[0038] Figure 3 This is graph 60, illustrating an exemplary FFT 62 of the electrical signal 40 received from the bandpass filter 50. Graph 60 includes an x-axis representing frequency in Hertz and a y-axis representing acoustic amplitude. Reference Figure 2 and Figure 3 The FFT module 52 of the control module 22 converts the electrical signal 40 from the bandpass filter 50 into various frequency components 66. For example... Figure 3 As shown, each frequency component 66 includes a peak amplitude 68 representing audible sound. Each frequency component 66 of the FFT 62 defines an amplitude trajectory 70. As described below, the trigger amplitude is determined based on the peak amplitude 68 of each frequency component 66 of the FFT 62.

[0039] The KDE function module 54 of the control module 22 uses the KDE function to analyze the amplitude trajectory 70 of the FFT 62. Figure 4 It is graph 80, which illustrates FFT 62 ( Figure 3 The smoothed estimate of the amplitude trajectory 82 is shown in Figure 80. Figure 80 includes an x-axis representing the acoustic amplitude and a y-axis representing the number of samples. Now refer to... Figure 2 , Figure 3 and Figure 4 KDE function module 54 executes the KDE function, which produces an FFT 62 ( Figure 3 The smoothed estimate of the amplitude trajectory 82 is given. The smoothed estimate of the amplitude trajectory 82 includes multiple peaks 84. Each peak 84 in the smoothed estimate of the amplitude trajectory 82 represents the peak amplitude 68 in the FFT 62( Figure 3 The number of times it appears in ().

[0040] The analyzer module 56 of the control module 22 determines the trigger amplitude based on a smoothed estimate of the amplitude trajectory 82. Specifically, the analyzer module 56 identifies the highest peak value 84A of the smoothed estimate of the amplitude trajectory 82. The highest peak value 84A represents the peak value in FFT62 (…). Figure 3 The most frequent peak amplitude is 68. It should be understood that... Figure 4 The highest peak 84A of the smoothed estimate of amplitude trajectory 82 shown represents the audible sound generated by background noise. Analyzer module 56 identifies a second highest peak 84B of the smoothed estimate of amplitude trajectory 82. The second highest peak 84B represents the peak amplitude 68 that appears most frequently in FFT 62 after the highest peak amplitude 68A. The second highest peak 84B represents the measured sample 12 ( ) when undergoing deformation before the material fractures. Figure 1 The sound emission emitted by ).

[0041] like Figure 4 As shown, the highest peak 84A and the second highest peak 84B are divided into two different groups, and the valley 90 is located between the two peaks 84A and 84B. The analyzer module 56 of the control module 22 determines the trigger amplitude by first identifying the valley 90 between the highest peak 84A and the second highest peak 84B, and then selecting the amplitude value corresponding to the valley 90 as the trigger amplitude. If there is no obvious interval between the two peaks 84A and 84B, it should be understood that acoustic emission has not yet occurred.

[0042] Figure 5 The diagram illustrates the use. Figure 1 The illustrated system 10 is a flowchart of an exemplary method 100 for determining the trigger amplitude. Now refer to... Figures 1 to 5 Method 100 begins at box 102. In box 102, control module 22 ( Figure 2 The monitoring microphone 20 generates an electrical signal 40. Method 100 can then proceed to box 104.

[0043] In box 104, control module 22 ( Figure 2 The bandpass filter 50 filters the electrical signal 40 generated by the microphone 20. Method 100 can then proceed to block 106.

[0044] In block 106, the FFT module 52 of control module 22 converts the electrical signal 40 from bandpass filter 50 into individual frequency components 66 (see...). Figure 3 Method 100 can then proceed to box 108.

[0045] In box 108, the KDE function module 54 of control module 22 uses the KDE function to analyze FFT 62 ( Figure 3 The amplitude trajectory of 70. Method 100 then proceeds to box 110.

[0046] In box 110, the analyzer module 56 of the control module 22 identifies the highest peak 84A of the smoothed estimate of the amplitude trajectory 82 (see...). Figure 4Method 100 then proceeds to box 112.

[0047] In block 112, the analyzer module 56 of control module 22 identifies the second highest peak 84B of the smoothed estimate of amplitude trajectory 82. Method 100 can then proceed to block 114.

[0048] In block 114, the analyzer module 56 of the control module 22 determines the trigger amplitude by identifying the valley 90 between the highest peak 84A and the second highest peak 84B. Method 100 can then proceed to block 116.

[0049] In block 116, the analyzer module 56 of control module 22 selects the amplitude value corresponding to the valley value 90 as the trigger amplitude. Method 100 can then terminate.

[0050] Figure 6 A system 200 for capturing images of a test sample 212 via camera 204 is illustrated. In an embodiment, camera 204 may be a stereo camera configured to capture three-dimensional images of the test sample 212; however, two-dimensional images may also be used. As described below, in response to determining that a trigger amplitude has occurred, system 200 instructs camera 204 to capture images at a faster frame rate. Therefore, by system 10 ( Figure 1 The determined trigger amplitude can be used to initiate a hardware trigger event, which triggers an external action (i.e., a change in the frame rate of camera 204). Although Figure 6 The illustration shows that system 200 uses trigger amplitude to initiate a change in the frame rate of camera 204, but it should be understood that trigger amplitude can also be used to initiate other events.

[0051] In such Figure 6 In the illustrated embodiment, system 200 includes a camera 204, a sample under test 212, a microphone 220, and a control module 222. The control module 222 communicates electronically with the camera 204 and the microphone 220. Similar to... Figure 1 In the embodiment shown, load 238 is applied to the test sample 212 via clamp 214, wherein load 238 causes the test sample 212 to undergo deformation before the material fractures.

[0052] Camera 204 captures images of the test sample 212. Specifically, camera 204 captures images at a first frame rate and a second frame rate, where the first frame rate is less than the second frame rate. It should be understood that camera 204 captures images at the first frame rate before system 200 determines that a trigger amplitude has occurred. Since the images captured by camera 204 are not very important, the first frame rate may be much slower than the second frame rate. Once control module 222 determines that a trigger amplitude has occurred, control module 222 instructs camera 204 to capture images at the second frame rate. The second frame rate is greater than the first frame rate because the images captured immediately after the acoustic emission from the test sample 212 are of the most interest. Collecting images at a faster frame rate once the acoustic emission occurs results in fewer images being collected, processed, and stored by control module 222.

[0053] Continue to refer to Figure 6 The control module 222 monitors the camera 204, which captures images at the first frame rate. The control module 222 also monitors the electrical signal 40 generated by the microphone 220 (see...). Figure 2 The control module 222 determines the trigger amplitude of the test sample 212 by measuring the electrical signal 40 generated by the microphone 220 during the test. In response to determining that the trigger amplitude has appeared, the control module 222 instructs the camera 204 to capture an image of the test sample 212 at a second frame rate.

[0054] In a non-limiting example, the first frame rate is approximately five frames per second (fps), and the second frame rate is approximately fifty fps. In this example, the total duration for which the camera captures images is approximately fifty-four seconds, and once a trigger amplitude is detected, camera 204 is instructed to switch to the second frame rate approximately fifty seconds after the start of the test. Therefore, camera 204 only captures images at the second frame rate for the last four seconds of the test. If the second frame rate were used for the entire duration, control module 222 would collect 2700 images. However, because camera 204 switches to the second frame rate 50 at the start of the test, only 450 images are collected. Therefore, in this example, instructing camera 204 to switch to the second frame rate results in a reduction of approximately eighty-three percent of the images collected, processed, and stored by control module 222. Accordingly, the disclosed system 200 provides a method for reducing the amount of image data stored and processed by control module 222.

[0055] The description in this disclosure is merely exemplary in nature, and variations thereof without departing from the spirit of this disclosure are intended to be within its scope. Such variations should not be considered as departing from the scheme and scope of this disclosure.

Claims

1. A system for determining a trigger amplitude indicative of a precursor to a material fracture in a specimen under test, the system comprising: a microphone that converts acoustic emissions emitted by the specimen under test into an electrical signal, wherein a load is applied to the specimen under test and the acoustic emissions are emitted when the load causes the specimen under test to experience a deformation prior to the material fracture; a control module in electrical communication with the microphone, wherein the control module executes instructions to: monitor the electrical signal produced by the microphone; filter the electrical signal produced by the microphone to allow frequencies within a range of interest and to attenuate frequencies outside the range of interest; convert the electrical signal produced by the microphone into individual frequency components based on a fast Fourier transform (FFT), wherein the individual frequency components each include a peak intensity representing audible sound; determine the trigger amplitude based on the peak intensities of the individual frequency components of the FFT, wherein the individual frequency components of the FFT define an amplitude trace; analyze the amplitude trace using a kernel density estimation (KDE) function, wherein the KDE function creates a smoothed estimate of the amplitude trace and the smoothed estimate of the amplitude trace includes a plurality of peaks, each peak representing a number of times a peak amplitude occurs in the FFT; identify a highest peak of the smoothed estimate of the amplitude trace, wherein the highest peak represents a peak amplitude that occurs most frequently in the FFT; identify a second highest peak of the smoothed estimate of the amplitude trace, wherein the second highest peak represents a peak amplitude that occurs most frequently in the FFT after the highest peak; determine a valley value between the highest peak and the second highest peak; and select an amplitude value corresponding to the valley value as the trigger amplitude.

2. The system of claim 1, wherein, the highest peak represents audible sound produced by background noise.

3. The system of claim 1, wherein, the second highest peak represents acoustic emissions emitted by the specimen under test when the specimen under test experiences a deformation prior to the material fracture.

4. The system of claim 1, wherein, the frequencies outside the range of interest represent background noise.

5. A method for determining a trigger amplitude, the trigger amplitude indicative of a precursor to a material fracture in a specimen under test, the method comprising: converting acoustic emissions emitted by the specimen under test into an electrical signal by a microphone, wherein a load is applied to the specimen under test and the acoustic emissions are emitted when the load causes the specimen under test to experience a deformation prior to the material fracture; a control module monitoring the electrical signal produced by the microphone; filtering the electrical signal produced by the microphone to allow frequencies within a range of interest and to attenuate frequencies outside the range of interest; converting the electrical signal produced by the microphone into individual frequency components based on a fast Fourier transform (FFT), wherein the individual frequency components each include a peak intensity representing audible sound; determining the trigger amplitude based on the peak intensities of the individual frequency components of the FFT, wherein the individual frequency components of the FFT define an amplitude trace; analyzing the amplitude trace using a kernel density estimation (KDE) function, wherein the KDE function creates a smoothed estimate of the amplitude trace and the smoothed estimate of the amplitude trace includes a plurality of peaks, each peak representing a number of times a peak amplitude occurs in the FFT; identifying a highest peak of the smoothed estimate of the amplitude trace, wherein the highest peak represents a peak amplitude that occurs most frequently in the FFT; identifying a second highest peak of the smoothed estimate of the amplitude trace, wherein the second highest peak represents a peak amplitude that occurs most frequently in the FFT after the highest peak; determining a valley value between the highest peak and the second highest peak; and selecting an amplitude value corresponding to the valley value as the trigger amplitude.

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