System and Method for Error Correction of Video Extensometer

Abstract

The present disclosure describes systems and methods for compensating for errors in video extensometer systems, including noise, perspective variation, and / or component placement and / or movement.

Description

Technical Field

[0001] [Related Applications] This application claims the benefit and priority of U.S. Provisional Application No. 63 / 180,288, filed May 27, 2021, entitled "Systems And Methods For Error Correction For Video Extensometers". The entire subject matter and content of U.S. Provisional Application No. 63 / 180,288 are hereby incorporated by reference in their entirety.

Background Art

[0002] Camera-based vision systems have been implemented as part of a material testing system for measuring specimen strain. These systems collect one or more images of the test specimen. These images are synchronized with other signals relevant to the test, such as specimen load, mechanical actuator / crosshead displacement, etc. The images of the test specimen are analyzed to identify and track the location of specific features of the specimen as the test progresses. By changes in the location of such features, such as changes in the relative positions of one or more reference features of the specimen, local specimen deformation can be calculated, and further, specimen strain can be calculated.

[0003] Conventional systems employ a camera or other imaging system to capture an image and measure the characteristics of the test specimen therefrom. However, differences between the recognized reference position and the actual position in imaging and / or measurement values can lead to distorted readings and inaccurate measurements. Thus, a system for correcting such errors is desired.

Summary of the Invention

[0004] Disclosed herein are systems and methods for correcting and / or compensating for imaging errors in a video extensometer system. These and other features and advantages of the present invention will become apparent from the following detailed description in conjunction with the appended claims.

[0005] The benefits and advantages of the present invention will become readily apparent to those skilled in the art after reviewing the following detailed description and accompanying drawings. [Brief explanation of the drawing]

[0006] [Figure 1] This is a block diagram of an extensometer system as an example according to the embodiments of this disclosure.

[0007] [Figure 2] This figure shows an example of a test sample measured in the extensometer system of Figure 1 according to an aspect of the present disclosure.

[0008] [Figure 3] This is a block diagram of another viewpoint of the extensometer system illustrated in Figure 1, according to an aspect of this disclosure.

[0009] [Figure 4] This is a block diagram of an implementation of an example of the extensometer system shown in Figure 1, according to the embodiments of this disclosure.

[0010] [Figure 5(A1)] This is a block diagram of an exemplary extensometer system according to the embodiments of this disclosure. [Figure 5 (A2)] This is a block diagram of an exemplary extensometer system according to the embodiments of this disclosure. [Figure 5 (A3)] This is a block diagram of an exemplary extensometer system according to the embodiments of this disclosure. [Figure 5 (A4)] This is a block diagram of an exemplary extensometer system according to the embodiments of this disclosure. [Figure 5 (A5)] This is a block diagram of an exemplary extensometer system according to the embodiments of this disclosure.

[0011] [Figure 6(B1)] This is a block diagram of an exemplary extensometer system according to the embodiments of this disclosure. [Figure 6 (B2)] This is a block diagram of an exemplary extensometer system according to the embodiments of this disclosure. [Figure 6 (B3)] A block diagram of an exemplary extensometer system according to an aspect of the present disclosure.

[0012] [Figure 7(C1)] A block diagram of an exemplary extensometer system according to an aspect of the present disclosure. [Figure 7(C2)] A block diagram of an exemplary extensometer system according to an aspect of the present disclosure. [Figure 7(C3)] A block diagram of an exemplary extensometer system according to an aspect of the present disclosure. [Figure 7(C4)] A block diagram of an exemplary extensometer system according to an aspect of the present disclosure. [Figure 7 (C5)] A block diagram of an exemplary extensometer system according to an aspect of the present disclosure. [Figure 7(C6)] A block diagram of an exemplary extensometer system according to an aspect of the present disclosure. [Figure 7 (C7)] A block diagram of an exemplary extensometer system according to an aspect of the present disclosure. [Figure 7(C8)] A block diagram of an exemplary extensometer system according to an aspect of the present disclosure.

[0013] [Figure 8 (D1)] A block diagram of an exemplary extensometer system according to an aspect of the present disclosure. [Figure 8 (D2)] A block diagram of an exemplary extensometer system according to an aspect of the present disclosure.

Mode for Carrying Out the Invention

[0014] The drawings are not necessarily to scale. Where appropriate, like or identical reference numerals are used to refer to like or identical components.

[0015] The present disclosure describes systems and methods for compensating for errors in a video extensometer system, including noise, range variation, and / or component placement and / or operation.

[0016] In particular, the disclosed examples provide systems and methods for addressing noise in a video extensometer system by employing a fluid delivery system, a vibration control system, and / or saturation control. Furthermore, the disclosed examples provide systems and methods for addressing imaging challenges in a video extensometer system by employing multiple cameras. Furthermore, the disclosed examples provide systems and methods for monitoring changes in a test specimen in a video extensometer system by monitoring a reference pattern and / or object. Furthermore, the disclosed examples provide systems and methods for mitigating thermal and / or external vibrations in a video extensometer system by employing compensation techniques including active, passive, and / or processing.

[0017] Conventional systems are subject to one or more errors when testing and measuring one or more physical properties of a test specimen. These errors may stem from limitations of the system components (e.g., physical / operational limitations of components, operational influences on related components, etc.), system calibration (e.g., for measuring different materials / samples), and / or measurement and / or analysis limitations (e.g., collection and analysis of measured properties, etc.).

[0018] Some conventional testing systems employ camera-based vision systems to capture information (e.g., measurements of one or more properties or geometric variables) during the material testing process (e.g., to determine the strain of a test specimen). Such systems can capture multiple images of the test specimen and synchronize these images with other information relevant to the testing process (e.g., specimen load, mechanical actuator / crosshead displacement, etc.). The images of the test specimen are then analyzed through one or more algorithms to identify and / or locate specific features of the test specimen (including reference features), and further track such features as the test operation progresses. Changes in the absolute and / or relative location of such features allow for the calculation of local specimen deformation, and consequently, the calculation of specimen strain.

[0019] The features of the sample being tested may consist of markings (e.g., reference features) applied to the surface of the test specimen that are visible to the camera(s). For example, a processor can analyze the image to determine the location and / or shape (and changes therein) of the markings and track their movement relative to each other during testing. Multiple markings may be present on the front surface of the specimen, such as pair groupings for determining gauge length-based strain measurements (axial marks, lateral marks, etc.) or pseudo-random speckle patterns used in digital image correlation (DIC) techniques. An alternative set of features that may be used for determining lateral strain of the specimen is the edge of the test specimen.

[0020] For single or multiple camera measurement systems, the calibration process can be performed on a selected calibration plane located at a predetermined distance from the image sensor. The calibration process establishes a relationship between one or more characteristics captured by the imaging device (e.g., size, position, width, etc.) and one or more physical characteristics on the calibration plane (e.g., determined by physical coordinates).

[0021] Such a calibration process may employ a calibration reference device placed on a calibration plane. The reference device includes predetermined physical properties having known geometric dimensions related to covering part or all of the field of view (FOV) in question. The calibration process allows an image of the calibration device to be captured and compared to the geometric shape of a known calibration device, where a transfer function is established to convert the image coordinates from a pixel coordinate system to a real-world physical coordinate system.

[0022] Conventional video extensometer systems track and measure the dimensions and / or relative location of markings on the surface of a test specimen. During the test process, an image processing algorithm is executed (e.g., via the video extensometer system's processor) to determine the location of the markings on the specimen's surface. Based on the determined location, the processor can calculate the initial gauge length of the specimen and the instantaneous change in the gauge length of the specimen from the initial value (which may be multiple) of the specimen (e.g., axial and / or lateral strain). The accuracy with which the video extensometer system can measure the absolute and / or relative position and / or positional change of the markings depends at least in part on whether the specimen's surface is coplanar with the initial calibration plane. The difference between the location of the measurement plane (corresponding to the surface of the test specimen) and the location of the calibration plane (corresponding to the reference plane) results in measurement errors (e.g., perspective errors). Larger deviations between the measurement plane and the reference plane (e.g., along the Z-axis between the specimen and the camera) result in larger measurement errors.

[0023] In some cases, multiple test specimens are subjected to a calibration process followed by a testing process, where each specimen has a different thickness, and / or the thickness of the test specimen changes during the test. As a result, the distance between the surface of the specimen and the imaging device changes during the testing process and / or from specimen to specimen.

[0024] Such distance errors can be more problematic in material testing applications where absolute dimensional measurements are required, compared to testing applications where measurements are used to determine proportional (e.g., ratiometric) strain. In determining proportional strain, distance errors reduce to similar proportional errors in the initial gauge length measurement and / or strain displacement measurement. Since strain is calculated as a displacement over the gauge length, distance errors are present in both the numerator and denominator and therefore cancel each other out. These distance errors can affect accuracy even when the strain is small, in which case the distance errors may become dominant over the intended strain measurement signal.

[0025] Conventional systems have attempted to mitigate some of these problems through various technologies, but each has significant drawbacks. One option is a calibration plane positioned on the mean or mid-plane of all test planes under consideration, so that perspective measurement errors are optimized across samples of different thicknesses. Another option is to compensate for different test samples by making physical adjustments to the sample mounting position of the extensometer to maintain a single working distance equal to that of the calibration plane. Yet another option is to use a telecentric optical system, which is less susceptible to out-of-plane perspective errors but is more expensive and has a more limited field of view. Yet another option is to employ multiple cameras to capture perspective information from various angles, which can be incorporated into the calibration and sample measurement process.

[0026] However, existing solutions to mitigate the perspective errors faced in video extensometer systems all have drawbacks. For example, using the average distance relative to the calibration plane and / or using less accurate measuring instruments inevitably results in less accurate measurements. Manually adjusting the extensometer mounting position to compensate for different sample thicknesses is time-consuming and requires operators to remember to consistently make multiple different adjustments for each sample based on its individual thickness. Furthermore, such adjustments are difficult to automate.

[0027] Telecentric optics are large, heavy, and expensive, and have a limited field of view (FOV). Therefore, video extensometer systems utilizing multiple cameras are expensive, complex, and require extensive 3D calibration processes and equipment.

[0028] The disclosed systems and methods, as a list of examples (not limited to those listed), mitigate systematic and deterministic errors in video extensometers resulting from variations in distance along the Z-axis, changes in the measurement plane relative to the calibration plane, and errors due to external noise including heat and vibration. In some examples, the errors are corrected in real time during the testing process.

[0029] As described herein, a materials testing system including a materials testing system that applies tension, compression, and / or torsion includes one or more components that produce displacement and / or load-bearing in order to apply stress to a test specimen and / or measure stress on a test specimen. In some examples, a video extensometer system is employed in a specimen strain test, which may include one or more of the following: collecting high-resolution images, providing the images to an image processor, analyzing the images to identify one or more specimen properties corresponding to displacement or strain values, and generating outputs corresponding to the properties.

[0030] Video processing employing an extensometer may include an external mechanical vision imaging device connected to a processing system or computing platform and / or video processing hardware, and may use software and / or hardware that converts data from the camera into electrical signals or has a software interface compatible with the material testing system.

[0031] Image devices employing camera-based image capture (e.g., vision or video) systems disclosed herein are implemented in material testing systems for measuring strain on a test specimen. Such a system collects multiple images of the test specimen (i.e., during the testing process), and these images are synchronized with other signals related to the test (e.g., sample load, mechanical actuator and / or crosshead displacement). The sample images are analyzed by algorithms (e.g., in real time and / or after the test) to identify and track specific sample characteristics as the test progresses. For example, changes in the location, size, shape, etc., of such characteristics enable the calculation of sample deformation, and consequently lead to the analysis and calculation of sample strain.

[0032] Accordingly, the systems and methods disclosed herein compensate for errors in a video extensometer system, including noise, perspective variations, and / or the arrangement and / or movement of components.

[0033] In the disclosed example, a system for correcting errors during a test process in a video elongation measurement system, the system comprising: a test system for fixing a test specimen; an imaging device positioned to capture an image of the surface of the test specimen; one or more sensors configured to measure one or more parameters related to the test specimen; and a processing system which receives image data from the imaging device; receives sensor data from one or more sensors; compares the image data or sensor data with one or more data thresholds; calculates a correction coefficient based in part on the image data and sensor data in response to the image data or sensor data exceeding one or more data thresholds; and instructs adjustments to the imaging device and system components based at least in part on the correction coefficient.

[0034] In some examples, the system components are active coolers, actuators, or imaging device positioning systems. In some examples, the imaging device is a single view camera. In some examples, the imaging device is two or more cameras.

[0035] In some examples, the sensor is an accelerometer, inertial measurement unit, temperature sensor, infrared sensor, light-emitting diode sensor, ultrasonic sensor, or laser sensor. In the examples, one or more parameters include one or more of the shape or position of the marking, the edge position of the test specimen, or the width of the test specimen. In the examples, the correction factor is expressed in millimeters, inches, or pixel units.

[0036] In some examples, the processing system is located on a remote computing platform that communicates with one or more of the test systems or imaging devices. In some examples, the processing system is integrated with one of the imaging devices or test systems.

[0037] In some disclosed examples, a system for correcting errors during the test process in a video elongation measurement system comprises: a test system for fixing a test specimen; an imaging device positioned to capture an image of the surface of the test specimen; one or more motion sensors configured to measure one or more motion parameters related to the video elongation measurement system; one or more actuators for adjusting the position or orientation of the imaging device; and a processing system which receives image data from the imaging device; receives sensor data corresponding to vibration measurements from one or more motion sensors; compares the image data or sensor data with one or more data thresholds; calculates a correction coefficient based in part on the image data and sensor data in response to the sensor data exceeding one or more data thresholds; and instructs one or more actuators to adjust the position or orientation of the imaging device, at least in part on the correction coefficient.

[0038] In some examples, one or more motion sensors include an accelerometer, an inertial measurement unit, a vibration sensor, or a tilt sensor. In some examples, one or more motion sensors are positioned near one or more image sensors to monitor and measure vibrations in one or more image sensors.

[0039] In some examples, one or more data thresholds correspond to one or more data threshold vibration values, and the processing system can further operate to correlate image data with vibration measurements that exceed one or more threshold vibration values. In some examples, the processing system can further operate to correct for excessive vibration by applying a compensation coefficient to the image data at correlated data points that exceed one or more threshold vibration values.

[0040] In some examples, the system includes a drive and control system that receives commands from a processing system to control one or more actuators. In one example, actuator 104 may include a piezoelectric actuator having a mechanical amplifier.

[0041] In some examples, measurement and compensation calculations can be performed in real time during the imaging operation. In some examples, active vibration control can be implemented in coordination with image data compensation.

[0042] In some examples, the processing system is located on a remote computing platform that communicates with one or more of the test system or imaging devices. In these examples, the imaging device includes two or more imaging devices, each capable of capturing an image of the surface of the test specimen.

[0043] Next, refer to the figure. Figure 1 shows an exemplary extensometer system 10 for measuring changes in one or more properties of a test specimen 16 undergoing mechanical property testing. This exemplary extensometer system 10 can be connected, for example, to a test system 33 capable of mechanically testing the test specimen 16. The extensometer system 10 can measure and / or calculate changes in the test specimen 16 undergoing, for example, a compressive strength test, a tensile strength test, a shear strength test, a bending strength test, a deflection strength test, a tear strength test, a peel strength test (e.g., the strength of an adhesive bond), a torsional strength test, and / or any other arbitrary compression and / or tensile test. In addition or alternatively, the material extensometer system 10 can perform dynamic testing.

[0044] According to the disclosed examples, the extensometer system 10 may include a test system 33 for manipulating and testing a test specimen 16, and / or a computing device or processing system 32 that is communication-coupled to the test system 33, a light source, and / or an imaging device, as further shown in Figure 4. The test system 33 applies a load to the test specimen 16 and measures the mechanical properties of the test, such as the displacement of the test specimen 16 and / or the force applied to the test specimen 16.

[0045] The extensometer system 10 includes a remote and / or integrated light source 14 (e.g., an LED array) and / or a reflective backscreen 18 for illuminating the test specimen 16. The extensometer system 10 also includes a processing system 32 (see also Figure 4) and a camera or imaging device 12. Although the example in Figure 1 shows a single camera 12, the disclosed examples are applicable to multiple camera extensometer systems 10. In some examples, the light source 14 and the imaging device 12 are configured to transmit and receive at infrared (IR) wavelengths, but other illumination sources and / or wavelengths are also applicable. In some examples, one or both of the light source 14 or the imaging device 12 include one or more filters (e.g., polarizing filters) and one or more lenses. In some examples, a calibration routine (e.g., a two-dimensional calibration routine) is performed and used to identify one or more characteristics of the test specimen 16 and one or more markers 20 (including marker patterns).

[0046] In the disclosed example, the computing device 32 can be used to configure the test system 33, control the test system 33, and / or process, display, report, and / or receive measurement data (e.g., transducer measurements such as force and displacement) and / or test results (e.g., peak force, break displacement) from the test system 33 for any other desired purpose. The extensometer system 10 connects to the test system 33 and software using a standard interface including Ethernet, analog encoder, or SPI. This allows the device to be plugged into an existing system and used by the existing system without requiring dedicated integrated software or hardware. The extensometer system 10 provides real-time axial encoder information and lateral encoder information or analog information to the material testing machine 33. The real-time video extensometer 10 and the material testing machine 33 exchange real-time test data, including extension / strain data, with an external computer 32 which can be configured via a wired communication channel and / or a wireless communication channel. The extensometer system 10 provides measurement and / or calculation of tensile / strain data captured from the test specimen 16 being tested in the material testing machine 33, and further provides stress data and tensile / strain data to the processor 32.

[0047] As disclosed herein, captured images are input from the imaging device to the processor 32. The processor uses one or more algorithms and / or lookup tables to calculate the stretch / strain values ​​of multiple axes of the test specimen 16 (i.e., the change or percentage change in inter-target distance calculated by image monitoring of markers 20 attached to the test specimen 16). After calculation, this data can be stored in memory or output to a network and / or one or more display devices, I / O devices, etc. (see also Figure 4).

[0048] Figure 2 shows an example test specimen 16 measured in the extensometer system 10 of Figure 1. For example, one or more markings 20 (e.g., reference features) are attached to a surface 28 facing the light source 14 and the imaging device 12. The gripping portion 26 is configured to be positioned within the grip of the test system 33 (see also Figure 4) and applies force to the test specimen 16. For example, a cross-member loader applies force to the test specimen 16 while the grip is holding the test specimen 16 or otherwise coupled to the test system 33. A force applicator, such as a motor, moves the crosshead relative to the frame and applies force to the test specimen 16 as indicated by the bidirectional arrow 34. The force 34 pulling the gripping portions 26 apart can stretch the test specimen 16, and as a result, the markings move from a first position 20A to a second position 20B. In addition, or instead, the markings may change in shape or size, and this change can also be measured by the processing system 32 by viewing the captured image. The force 34 can also move the edge of the test specimen from the first position 22A to the second position 22B. For example, in the first position, i.e., the initial position, the edge has a width 24A, and when the force 34 is applied, it decreases to a width 24B.

[0049] Based on the captured image, the processing system 32 is configured to perform stretching / strain in the measurement process. For example, to detect stretching / strain in the test specimen 16, the processing system 32 monitors the image provided via the imaging device 12. Once the processing system 32 identifies a change in the relative position between two or more markers and / or edges of the test specimen 16 (compared to their initial location at the start of the crosshead movement, for example), the processing system 32 measures the amount of change to calculate the amount of stretching and / or strain in the test specimen 16. As disclosed herein, the markers are configured to reflect light from a light source to the camera, while the back screen reflects light to generate a dark silhouette for edge analysis.

[0050] As disclosed herein, the video extensometer system 10 is configured to perform optical width measurement of an opaque test specimen 16. The imaging device 12 is positioned to observe the surface 28 of the test specimen 16 facing the imaging device 12. The surface 28 is located near the focal plane of the imaging device optical instruments (see, for example, Figure 3).

[0051] As shown in Figure 3, the video extensometer system 10 is arranged to measure either or both of the axial strain (based on markers 20 and / or changes in the pattern of markers on the front surface 28 of the test specimen 16) and the lateral strain (calculated from changes in the width of the specimen 16). The components of the video extensometer system 10 are shown in a top perspective view of Figure 3, where the location of each component relative to the other components is schematic. As shown, the components include an imaging device 12 (e.g., a video camera) configured to capture one or more images of the test specimen 16 during a physical test (e.g., at regular intervals, continuously, and / or based on one or more thresholds associated with time, force, or other suitable test characteristics).

[0052] As shown in the figure, the imaging device 12 and the test sample 16 are positioned at a working distance or Z-axis distance 39, which may be static, predetermined, and / or changeable during the test process.

[0053] The test specimen 16 is characterized by appropriate marks or reference features 20 on the forward surface 28 (and / or opposing surface) of the test specimen 16. Analysis of one or more images associated with the video extensometer system 10 is performed via the processing system 32, and an identification algorithm is executed that enables continuous tracking and measurement of both the markings 20 of the test specimen 16 and the edges 22 of the test specimen during the testing process.

[0054] In the illustrated example, the imaging device 12 is a single-view camera having a single optical axis 50. In some examples, two or more imaging devices may be used, which may be arranged and / or positioned at different field-of-view angles of the test specimen 16. By employing a stereo imaging configuration, measurement variables related to the distance and / or depth of multiple dimensions of the test specimen 16 may also be used to further calibrate and / or measure the characteristics of the test specimen 16.

[0055] In some examples, the measurements and / or positions of one or more edges are provided in pixel coordinates captured by the imaging device 12. In addition to or alternatively, the measurements and / or positions of one or more edges are provided in other standard coordinate systems / units, such as meters. In such examples, a calibration process may be implemented to determine the absolute and / or relative positions and / or dimensions of the test specimen in the test system prior to measurement, and similar coordinate systems / units may be employed during the test process.

[0056] Figure 4 is a block diagram of an extensometer system 10 illustrating an example in Figure 1. As shown in Figure 1, the extensometer system 10 includes a test system 33 and a computing device 32. The illustrative computing device 32 can be a general-purpose computer, a laptop computer, a tablet computer, a mobile device, a server, an all-in-one computer, and / or any other type of computing device. The computing device 32 in Figure 4 comprises a processor 202, which can be a general-purpose central processing unit (CPU). In some examples, the processor 202 can include one or more dedicated processing units such as an FPGA, a RISC processor with an ARM core, an image processing unit, a digital signal processor, and / or a system on a chip (SoC). The processor 202 executes machine-readable instructions 204 that can be stored locally in the processor (e.g., in an internal cache or SoC), in random access memory 206 (or other volatile memory), in read-only memory 208 (or other non-volatile memory such as flash memory), and / or in a mass storage device 210. The illustrated mass storage device 210 can be a hard drive, a solid-state storage drive, a hybrid drive, a RAID array, and / or any other mass data storage device. Bus 212 enables communication between the processor 202, RAM 206, ROM 208, mass storage device 210, network interface 214, and / or input / output interface 216.

[0057] An example network interface 214 includes hardware, firmware, and / or software for connecting the computing device 201 to a communication network 218, such as the Internet. For example, the network interface 214 may include IEEE 202.X compliant wireless and / or wired communication hardware for transmitting and / or receiving communications.

[0058] The I / O interface 216 illustrated in Figure 4 includes hardware, firmware, and / or software that connects one or more input / output devices 220 to the processor 202 to provide input to the processor 202 and / or output from the processor 202. For example, the I / O interface 216 may include an image processing device interfaced with a display device, a universal serial bus port interfaced with one or more USB-compliant devices, FireWire®, a fieldbus, and / or any other type of interface. The extensometer system 10 in the example includes a display device 224 (e.g., an LCD screen) coupled to the I / O interface 216. Other illustrative I / O devices 220 may include a keyboard, keypad, mouse, trackball, pointing device, microphone, audio speaker, display device, optical media drive, multitouch touchscreen, gesture recognition interface, magnetic media drive, and / or any other type of input and / or output device.

[0059] The computing device 32 can access the non-temporary machine-readable media 222 via the I / O interface 216 and / or I / O device(s) 220. Examples of the machine-readable media 222 in Figure 4 include optical discs (e.g., Compact Disc (CD), Digital Versatile / Video Disc (DVD), Blu-ray® disc, etc.), magnetic media (e.g., floppy disks), portable storage media (e.g., portable flash drives, Secure Digital (SD) cards, etc.), and / or any other type of removable and / or installed machine-readable media.

[0060] The extensometer system 10 further comprises a test system 33 coupled to a computing device 32. In the example in Figure 4, the test system 33 is coupled to the computing device via an I / O interface 216 such as a USB port, Thunderbolt port, FireWire® (IEEE 1394) port, and / or any other type of serial or parallel data port. In some examples, the test system 33 is coupled to a network interface 214 and / or I / O interface 216 via a wired or wireless connection (e.g., Ethernet, Wi-Fi, etc.) directly or via a network 218.

[0061] The test system 33 comprises a frame 228, a load cell 230, a displacement transducer 232, a crossmember loader 234, a material fastener 236, and a control processor 238. The frame 228 provides rigid structural support for the other components of the test system 33 that perform the test. The load cell 230 measures the force applied to the material under test by the crossmember loader 234 via a grip 248. The crossmember loader 234 applies force to the material under test, while the material fastener 236 (also referred to as a grip) grips the material under test or otherwise connects the material under test to the crossmember loader 234. The exemplary crossmember loader 234 comprises a motor 242 (or other actuator) and a crosshead 244. As used herein, “crosshead” refers to a component of the material test system that applies directional (axial) force and / or rotational force to a specimen. The material testing system may have one or more crossheads, and the crossheads (which may be more than one) can be positioned in any suitable location and / or orientation within the material testing system. The crosshead 244 connects the material fixture 236 to the frame 228, and the motor 242 moves the crosshead relative to the frame to position the material fixture 236 and / or apply force to the material under test. Exemplary actuators that can be used to provide force and / or motion to the components of the extensometer system 10 include electric motors, pneumatic actuators, hydraulic actuators, piezoelectric actuators, relays, and / or switches.

[0062] The example test system 33 uses a motor 242 such as a servo motor or a direct-drive linear motor, but other systems may use different types of actuators. For example, hydraulic actuators, pneumatic actuators, and / or any other type of actuator may be used depending on the requirements of the system.

[0063] The example grip 236 includes a compression platen, jaws, or other types of fasteners, depending on the mechanical properties being tested and / or the material under test. The grip 236 can be manually configured, controlled via manual input, and / or automatically controlled by the control processor 238. The crosshead 244 and the grip 236 are components accessible to the operator.

[0064] The extensometer system 10 may further comprise one or more control panels 250, each equipped with one or more mode switches 252. The mode switches 252 may include buttons, switches, and / or other input devices located on the operator control panel. For example, the mode switch 252 may include a button that controls a motor 242 to jog (e.g., position) the crosshead 244 to a specific position on the frame 228, a switch (e.g., a foot switch) that controls a grip actuator 246 to open and close a pneumatic grip 248, and / or any other input device that controls the operation of the test system 33.

[0065] The exemplary control processor 238 communicates with the computing device 32, for example, to receive test parameters from the computing device 32 and / or to report measurements and / or other results to the computing device 32. For example, the control processor 238 may include one or more communication interfaces or I / O interfaces that enable communication with the computing device 32. The control processor 238 can control the crossmember loader 234 to increase or decrease the applied force, control the fixture(s) 236 to grip or release the material under test, and / or receive measurements from the displacement transducer 232, load cell 230 and / or other transducers.

[0066] The exemplary control processor 238 is configured to perform the stretch / strain measurement process while the test specimen 16 is being tested in the test system 33. For example, to detect stretch / strain in the test specimen 16, the control processor 238 monitors images provided via the imaging device 12. Once the control processor 238 identifies a change in the location and / or position of the edge 22 of the test specimen 16 (e.g., compared to its initial location at the start of the movement of the crosshead 244), the control processor 238 measures the amount of change to calculate the amount of stretch and / or strain in the test specimen 16. For example, real-time video provided by the imaging device 12 captures the absolute position of the edge 22 and monitors their relative movement across several images to calculate stretch / strain in real time. Stress and strain data are exchanged between the real-time video extensometer 10, the test system 33, and the processing system 32, typically compiled, and displayed via the display device 224.

[0067] Fluid delivery, vibration control, vibration compensation, chrominance control fluid delivery Some exemplary systems operate in an environment that includes an imaging device or camera, lighting, a test platform, and a sample. The operation of the system generates and / or receives heat from the environment, which may result in temperature differences within the test environment. For example, this may result in the formation of a thermal boundary layer in the air near the lens (and / or at one or more locations between the lens and the test sample), and the air having a changing density in the region immediately in front of the lens (and / or at one or more locations between the lens and the test sample). This then increases the possibility of measurement errors due to light distortion effects (e.g., "mirage" errors, light refraction, etc.) during the imaging operation.

[0068] In some disclosed examples, as shown in Figure 5(A1), a directional gas outlet 64 (e.g., a fluid discharge device, air / gas nozzle, air / gas knife, etc.) provides one or more gases / fluids from a fluid source 62 to replace, mitigate, regulate, and / or flow the air and the resulting thermal boundary layer (high temperature and / or low temperature). In this way, the gas outlet 64 can mix the air in front of the lens with the ambient air in the test area. As a result, variations in air density (e.g., temperature differences) are reduced, thereby reducing the possibility of mirage errors.

[0069] In some cases, the adoption of a gas outlet 64 can increase the frequency associated with noise observed during imaging by moving and / or mixing air (e.g., at or near the lens, between the lens and the test specimen). The increased frequency allows for simplified filtering (e.g., via digital filters, software and / or hardware filters, etc.) to remove the effects of noise from the processed data (e.g., imaging measurements, etc.).

[0070] In some cases, the use of the gas outlet 64 helps to redistribute air at various temperatures, including by applying gas / air at temperatures within a threshold amount of ambient temperature. Furthermore, the application of gas / air can wash away dust, particulate matter, condensates, and / or other objects (e.g., insects) that may adhere to the lens.

[0071] Active vibration control - A Some example systems operate in environments that include external sources that can affect the operation of system components such as cooling fans, cameras, lighting, test platforms, and samples. System operation can generate vibrations, which may result in movement of the image sensor / lens relative to the sample during the test process, subsequently leading to noisy image data. Vibration modes that do not produce common-mode effects are those that significantly impact the test data.

[0072] In the disclosed example, as shown in Figure 5(A2), an active vibration cancellation unit 68 may be installed in the system to reduce local vibrations in the image sensor 70, thereby reducing vibrations and / or inductive noise in the image signal.

[0073] In some examples, one or more linear and / or rotary axes may be measured and / or mitigated. For example, one or more sensors may be employed (e.g., to measure acceleration, optical adjustment, etc.), and there may be multiple associated actuators to respond to vibrations in each of the monitored axes.

[0074] In alternative or additional examples, a tuned mass damper system (e.g., a passive system) may be employed in conjunction with (or instead of) an active vibration cancellation module.

[0075] Active vibration control - B In additional or alternative examples, as shown in Figure 5(A3), the PCB 66 on which the image sensor 70 is mounted is itself mounted on another substrate 72 or housing wall. The active vibration cancellation unit 68 may be mounted on the PCB 66, the PCB mounting fastener 74, and / or between the PCB 66 and the PCB mounting fastener 74. According to the disclosed examples, the active vibration cancellation unit 68 can respond to vibrations (e.g., based on motion sensor feedback, etc.) to reduce local vibrations to the image sensor 70 and reduce induced noise on the image signal.

[0076] Furthermore, in some examples, the active vibration cancellation unit 68 may operate simultaneously with and / or be replaced by the tuned mass damper system. As used herein, the tuned mass damper system (e.g., a harmonic absorber or seismic damper) is a device or system that can be connected to or otherwise mounted on the PCB 66 and / or substrate 72 and is used to reduce vibration. The tuned mass damper system may include a mass element mounted on a damping spring, having a vibration frequency tuned to the same frequency as the resonant frequency of the system during operation.

[0077] active vibration compensation In additional or alternative examples, the motion sensor 76 (e.g., inertial measurement unit, accelerometer, etc.) may be mounted on the PCB 66, for example, by being positioned near the image sensor 70 (e.g., on a common surface of the PCB 66, on the opposite surface, etc.), as shown in Figure 5(A4). In some examples, the measurements from the motion sensor 76 are provided to a control circuit unit or other processor (e.g., processing system 32, processor 202, control processor 238, etc.). The processing system 32 correlates the timing of the imaging process with the measurements from the motion sensor 76. If the measurements from the motion sensor 76 exceed, for example, a filter threshold (e.g., based on physical motion, tolerance for data / image acquisition, etc.), the processing system 32 may compensate for errors due to excessive motion by firmware, software, and / or hardware methods, etc.

[0078] lighting compensation Some example systems are configured to optimize the illumination directed at the system to meet specific illumination standards. For example, sufficient illumination of the test specimen reduces noise and / or imaging errors during imaging operations.

[0079] In some systems, the amount of illumination (e.g., intensity, saturation level, etc.) is fixed, limited to manual adjustment of the light source, and / or cannot be adjusted during the imaging operation. Due to these limitations, suboptimal lighting conditions, including image saturation, can lead to a decrease in image accuracy and precision.

[0080] To address these shortcomings, the disclosed system is configured to measure the image saturation of each test sample 16 prior to the start of the test operation, as shown in Figure 5(A5). As the image operation progresses, the image data is analyzed (e.g., via the image sensor 70 in the camera 12, in the processing system 32, etc.) and compared to one or more thresholds (e.g., light intensity in the image). In response to exceeding a threshold, the intensity of the light source 14A may be automatically adjusted to provide the test sample with a desired level of image saturation. In some examples, one or more photosensitive sensors are employed to measure light intensity (e.g., in the test sample 16, in the camera 12, etc.), and these photosensitive sensors may be employed by the processing system 32 to determine adjustment values ​​for the light source.

[0081] Movement in the Z-axis direction In some video extensometer systems, the optical axis from the camera to the sample being measured is called the "Z-axis," and the test sample is imaged in the XY plane. When using an entocentric lens, a measured change in the distance between the camera and the test sample will change the imaged dimensions of the test sample. In the case of a video extensometer that measures displacement between multiple reference features (e.g., 2 points, 4 points, etc.), a change in the position of the sample in the Z-axis direction will cause a change in the imaged dimensions between the reference points that is not related to the test operation, leading to errors, for example, in strain measurement. However, the disclosed examples provide several methods and systems for reducing and / or eliminating errors related to errors in the Z-axis direction.

[0082] Telecentric lens In some exemplary systems, the video extensometer system employs one or more conventional optical lenses with an angular field of view. As a result, the imaging suffers from parallax errors, which can increase or decrease the magnification of the test specimen being measured as the object moves toward or away from the lens. In the disclosed examples, the video extensometer system employing one or more telecentric lenses mitigates this error by having a non-angular, constant field of view.

[0083] In some disclosed examples, the video extensometer system employs two or more cameras, at least one of which is fitted with a telecentric lens having a relatively small field of view (50mm to 90mm, e.g., camera 12B in Figure 7(C6)). Another camera (or more) (e.g., camera 12 as shown in the figure) employs an entocentric lens having a different field of view, which may be larger than the field of view associated with the telecentric lens.

[0084] During imaging, errors caused by the movement of the test specimen in the Z-axis direction are generally more pronounced in the early stages of the testing process (contrary to later stages). In the disclosed system, the first camera using a telecentric lens will not experience changes in image dimensions (e.g., with respect to one or more reference features) caused by movement in the Z-axis direction. As the distortion applied to the test specimen increases (e.g., distortion of a threshold amount, expansion of the test specimen, exceeding a certain time, and / or in response to an instruction), the system transitions to an analysis using measurements acquired by the remaining camera employing an entocentric lens.

[0085] Projected patterns In some examples, the video extensometer system may measure the motion in the Z-axis direction by analyzing changes related to features of the test specimen, independent of the deformation of the test specimen. For example, an image or other feature may be projected onto the surface of the test specimen. For example, a laser 78 and / or other type of projector may project a feature (e.g., dots, lines, patterns, etc.) as shown in Figure 6(B1). The lens 15 and image sensor 70 can measure the motion in the Z-axis direction by measuring the change and / or displacement of the projected feature, for example, by using a known angle α between the projected light and the surface of the test specimen.

[0086] For example, system 10 may employ a sensor 70 to measure one or more features of the arrangement of the test system 10. For example, sensor 70 may employ one or more methods (e.g., infrared (IR) light, light-emitting diode (LED) output, ultrasonic sensor, structured light imaging, time-of-flight calculation, laser sensor, etc.) to sense the measurement distance along the Z axis between the imaging device 12 and the test sample 16. The results may be transmitted from sensor 70 to a processor circuit or computing device (e.g., a processing system 32 via an interface) for analysis. The processing circuit may then generate and apply a correction coefficient based on the difference along the Z axis.

[0087] Therefore, the camera 12, image sensor 70, and processing system 32 can be used for both tracking reference features of the test specimen and processing measurements of projected features / patterns. To determine the amount of movement in the Z-axis direction, the change in the projected features may be calculated in the processing system 32. Error correction values ​​can then be calculated and / or determined (for example, by referring to a list of corresponding changes in the Z-axis direction for a compensation coefficient) and applied to measurements related to each change in the reference feature(s) to improve the test results.

[0088] structured lighting projector In one example, a projection method (e.g., a digital photoprojection) may be employed to project a predetermined pattern onto the surface of the test specimen. During the imaging operation, the movement of the specimen in the Z-axis direction will result in distortion of the pattern (e.g., a grid, a collection of parallel lines, etc.). Therefore, imaging and measuring the geometric deformation of the pattern provides data related to the movement in the Z-axis direction. Advantageously, the deformation can also provide information related to other deformations during the test operation, such as warping of the test specimen.

[0089] Laser triangulation sensor In some disclosed examples, as shown in Figure 6(B2), a laser triangulation sensor (or more) 88 is used to detect the movement of the test specimen 16 in the Z-axis direction. For example, a laser light source 82 may generate laser light 87 directed at the test specimen 16 through one or more lenses 84. The reflected laser light 87A is received by a photodetector 88 (e.g., a photosensitive sensor), which is configured to generate a signal proportional to the magnitude of the movement in the Z-axis direction. The signal from the photodetector 88 is analyzed in a processing system 32 to calculate a compensation coefficient to correct this movement.

[0090] For example, one or more characteristics of the received light (e.g., phase, intensity, frequency, etc.) may be correlated with the change in the Z-axis direction.

[0091] Reference object In some exemplary systems, as shown in Figure 6(B3), the reference scale object 90 may be positioned together with and / or near the test specimen 16. When the test specimen 16 is tested, the absolute and / or relative positions of the reference features 14 change, but the reference scale object 90 and associated reference features 14A remain stationary. The image sensor 70 can capture data on the test specimen 16 and / or the reference scale object 90 during the imaging operation.

[0092] For example, any change in the absolute and / or relative size of the reference scale object 90 is caused by movement in the Z-axis direction, which can be calculated and corrected. Furthermore, if a change in the Z-axis direction is detected in the test sample 16 but not in the reference scale object 90, the relative change between the two objects may represent a change in the Z-axis direction relative to the test sample 16, which can be calculated via the processing system 32.

[0093] Multiple cameras As a tool to provide highly accurate results, video elongation measurement systems face several challenges. Generally, strain calculation during elongation measurement tests assumes that the measurements taken during the test are accurate measurements of the (often two-dimensional) changes in the test specimen. These dimensions refer to how the shape of the specimen changes in the axial dimension (X-axis) and the lateral dimension (Y-axis). In video elongation measurement, the camera provides two-dimensional measurements by positioning it and focusing it on the surface of the test specimen in order to directly observe the changes in the X and Y axes of the specimen.

[0094] However, in actual test environments, there may also be changes in the Z-axis (for example, the distance from the camera to the test sample) that can affect the accuracy of measurements in the X and Y directions.

[0095] For example, if the test specimen 16 is set up before the start of the test, as shown in Figure 7(C7), the test specimen 16 may be positioned at a Z-direction distance 39B from the camera 12, which is different from the Z-direction distance 39A at which the camera 12 was last calibrated. If the test specimen is set up before the start of the test, as shown in Figure 7(C8), the test specimen may be set up so that it is not perfectly vertical, thereby resulting in the upper part 97A and the lower part 97B of the test specimen being at different Z-direction distances 39C and 39D from the camera, respectively. At the start of the test, the test specimen itself may not have a consistent Z-direction distance / shape across the entire surface of the specimen. For example, depending on how the test specimen was fabricated, or due to physical stresses induced during the test setup, the central part may be warped. When the tracked reference feature moves in the Z-axis direction relative to the calibration plane, this can result in perspective errors (e.g., phantom distortion). Such perspective errors can outweigh true data, especially in regions with low distortion (e.g., the elastic region of relatively hard materials), potentially leading to inaccurate measurements.

[0096] During the test, one or more factors, such as physical conditions, the sample's response to the test conditions, or the action of test components (e.g., interaction with the grips holding the sample), may lead to some or all of the test sample changing in the Z-axis direction.

[0097] In some cases, there is a balance between accuracy and field of view in video elongation measurement systems. The camera used for video elongation measurement has a certain inherent image resolution. This resolution affects the accuracy of the two-dimensional measurements calculated from the image. The lens used in the system can map this inherent image resolution to the field of view of the test space. Using magnification to increase the field of view size comes with a trade-off with a decrease in accuracy.

[0098] As the test progresses, some specimens may change significantly during the test (e.g., an increase in one or more dimensions), and by the end of the test, the field of view required to cover the entire specimen may be considerably larger than the field of view at the start of the test. This often occurs with so-called "highly elongated" materials, but can apply to a variety of materials.

[0099] Some material tests require greater accuracy at the start of the test than at the end. However, the need for a video elongation measurement system to cover a larger field of view throughout the entire test limits the available measurement accuracy compared to when it can focus on a smaller field of view at the start of the test.

[0100] To address these and other causes of error, various system configurations are disclosed, such as employing multiple cameras for measuring distortion.

[0101] In some examples, the multi-camera video elongation measurement system 10 employs one or more forward-facing cameras and one or more side-facing cameras. In the example shown in Figure 7(C1), as disclosed herein, the forward-facing camera 12 is used to measure changes in a reference feature 20. A side-facing camera 12A is added to track changes in the Z-axis direction between the camera 12 and the test specimen 16.

[0102] In this way, the side-facing camera 12A can track the movement of the test specimen 16 as it approaches or moves away from the forward-facing camera 12. The test specimen 16 may tilt during the test operation, and the first or upper part and the second or lower part of the test specimen may have different distances in the Z direction from the forward-facing camera 12 (e.g., a constant or changing angle during the test), but may maintain a linear relationship between the first and second parts.

[0103] In some cases, the shape of the test specimen 16 may change in the Z-axis direction during the test, thereby causing the specimen's shape to bend inward or outward during the test, and / or to start in a bent state but become straight during the test.

[0104] In some examples, lateral measurements may be used to enable and / or disable the accuracy of operations prior to and / or after the test, to provide interactive information to the operator while loading the sample to guide the setup, to correct (or compensate for) positional data generated by the forward-facing camera after the test is completed, and to correct (or compensate for) positional data generated by the forward-facing camera in real time during the test operation, and / or may be configured with a forward or rear lighting scheme for direct or silhouette image capture. For example, to measure the distortion of the test sample 16, measurements from both the forward-facing camera 12 and the rear-facing camera 12A may be provided to the processing circuit 32.

[0105] In some examples, as shown in the example in Figure 7(C2), the video elongation measurement system 10 employs two or more forward-facing cameras 12, 12B having similar imaging and / or sensing capabilities, each camera mounted at an angle slightly offset from the others but having the same field of view (e.g., of the test specimen 16). Although Figure 7(C2) shows the use of two cameras, additional cameras (e.g., three, four, or more) may be included. For example, each camera can be positioned at various distances, at various angles to other cameras and / or the test specimen, and / or have various optical properties (e.g., focal length, magnification, refractive power, etc.).

[0106] In this example, multiple cameras can, for example, collect stereo images for offline three-dimensional (3D) digital image correlation (DIC) image analysis. For example, a first camera may be used to capture a reference feature position in the XY plane, and images from a second camera (and / or three or more cameras) may be used to establish movement in the Z-axis direction as a cross-check. The cross-check can provide feedback to the processing circuit (e.g., processing system 32) and / or operator to correct and / or compensate for changes in the reference feature position in the XY plane.

[0107] In some cases, two or more cameras are employed simultaneously to collect 3D stereo images for real-time 3D reference feature tracking. For example, the views from each camera can be used to cross-check camera calibration, notify the system to verify calibration, and / or notify it of any necessary recalibration. As shown in the example in Figure 7(C3), the overlap between field of view 95A and field of view 95B (corresponding to the upper portion 97A and the lower portion 97B, respectively) can provide additional data for algorithmically filtering noise, thereby improving imaging accuracy.

[0108] In some examples, as shown in Figure 7(C6), two forward-facing cameras 12 and 12B are employed, with the first camera 12 having a wide field of view 95C and the second camera 12B having a narrow field of view 95D that is included within the field of view of the first camera.

[0109] The first wide-field camera is configured to capture the entire range of sample movement during the test, but has lower resolution than the second camera. Thus, the second narrow-field camera provides a relatively high-resolution view of the gauge-length portion of the sample at the start of the test, although some of it may move out of this camera's field of view during the test.

[0110] Combining image data from the first and second cameras optimizes image capture as the test sample changes, allowing the measurement to maintain image resolution and displacement accuracy during testing, especially for highly elongated samples.

[0111] Furthermore, comparing the overlapping portions of each field of view provides correction in the Z-axis direction. For example, the overlapping portions and associated Z-axis direction correction provide high-resolution imaging of the portion of the Z-axis movement during the initial sample loading and testing.

[0112] In some examples, as commonly shown in the example in Figure 7(C3), two or more forward-facing cameras 12, 12B are employed, the first camera 12 having a first field of view 95A covering a portion slightly larger than 50% of the first or upper portion 97A of the test space (or test specimen), and the second camera 12B having a second field of view 95B covering a portion slightly larger than 50% of the second or lower portion 97B of the test space (or test specimen), with some overlap between the first and second fields of view.

[0113] In this example, the use of the first and second fields of view nearly doubles the amount of distance at which highly elongated samples can be measured.

[0114] Furthermore, the overlapping portion of the two fields of view between the two cameras provides a certain degree of stereo vision, facilitating Z-axis measurement and therefore correction, as disclosed herein. The overlapping portion provides information about Z-axis motion, and this information enables measurement and / or correction during loading and / or testing of the test specimen.

[0115] In some cases, the system can be expanded to include more cameras (e.g., three, four, or more) and some overlap between adjacent fields of view to cover larger stretch-displaced samples.

[0116] In some disclosed examples, as shown in the example in Figure 7(C2), multiple (e.g., two or more) forward-facing cameras are employed, with one or more cameras configured to adjust their field of view. This adjustment may be automatic and / or instructed by an operator (e.g., in response to sensor measurements) and can be incorporated in real time during the calibration step and / or during the test process.

[0117] Adjustments to the field of view may include, but are not limited to, one or more changes to camera magnification, camera or lens position and / or orientation (e.g., vertical and / or horizontal positioning).

[0118] In some examples, each camera may be focused to track a single reference feature (e.g., a marking, dot, etc.). Each reference feature will be imaged at high resolution (e.g., the maximum value provided by the camera or associated optical system) to accurately track the position of the reference feature as it moves and / or changes during the test.

[0119] Depending on the type of sample and / or the need for material testing, the system may employ a single camera configured for adjustment (e.g., optically and / or physically), while the other camera is fixed (e.g., fixed magnification, position, and / or orientation). In some examples, each camera is configured for adjustment as disclosed herein. Some exemplary systems may employ a single camera configured for adjustment (e.g., magnification, position, and / or orientation) without employing a second camera.

[0120] In some examples where two or more forward-facing cameras are employed, the first camera has a first field of view covering a first or upper portion of the test space (or test specimen), and the second camera has a second field of view covering a second or lower portion of the test space (or test specimen), with no overlap between the first and second fields of view.

[0121] In some examples, the first camera and the second camera can each be focused on a unique first reference feature 21A or a second reference feature 21B. For example, the first reference feature 21A is located at the top 97A and the second feature 21B is located at the bottom 97B.

[0122] Each reference feature may have specific characteristics (e.g., size, shape, location, position, etc.) relative to other reference features (within the limitations of the test space and calibration procedure) at the start of the calibration stage and / or the test process. This provides high resolution and accuracy for test specimens with relatively large initial gauge lengths. Measurements from each camera may then be provided to a processor (e.g., processing system 32) for analysis that takes into account predetermined relationships between the cameras (e.g., their placement in the test environment).

[0123] In some examples, multiple fields of view may be captured by a single camera, providing similar and / or various advantages associated with systems employing multiple cameras, without the need and / or cost of employing multiple sets of lenses, cameras, and / or image sensors.

[0124] In the example, as shown in Figure 7(C4), one or more external mirrors 92 are positioned around the test specimen 16 and / or camera 12. The one or more mirrors 92 are positioned so that a single camera 12 can view the test specimen 12 and the mirrors 92 without moving and / or adjusting the focus or position of the camera 12. Thus, the test specimen 12 and / or the mirrors 92 can be viewed simultaneously (and / or periodically, alternately, and / or in response to instructions).

[0125] In some examples, as shown in Figure 7(C5), multiple mirrors 92A and 92B are used to view the test specimen 12 from various angles (e.g., the side of the test specimen, the opposite side of the test specimen, etc.). The mirrors (there may be more than one) 92 may be designed to provide the same level of magnification as direct viewing of the test specimen 16 by the camera 12, or they may be designed to provide different levels of magnification.

[0126] In some examples, one or more internal optical systems 93 (e.g., prisms, mirrors, diffraction gratings, filters, etc.) may be used to manipulate the received light (e.g., within a camera 12, test system 33, etc.). Instead of using a dedicated image sensor for each lens, light from multiple lenses or other optical systems (e.g., mirrors) may be directed to different parts of a single image sensor 70. In some examples, light received from a single lens 15 may be duplicated (e.g., split, reflected, etc.) onto multiple image sensors to improve noise.

[0127] In some examples, light from a single lens 15 or camera 12 may be split and reoriented onto different parts of the same image sensor and / or onto different image sensors, taking into account one or more properties of the light (e.g., phase, frequency, etc.), in order to improve noise and / or provide information in the Z-axis direction. For example, by employing multiple lenses, different frequency filters can be used for each lens. This information can then be combined with other optical data, such as spatial frequency separation provided by a prism.

[0128] In some cases, a color image sensor may be used in addition to, or instead of, a monochrome sensor. For example, replacing a monochrome sensor with a color image sensor allows for imaging and processing of the test sample using light of multiple frequencies simultaneously. Advantageously, employing a color image sensor instead of a monochrome sensor simplifies the calibration process, reduces costs, and simplifies the reference feature tracking algorithm. Using a sensor configured to receive light of multiple frequencies can further improve noise reduction and / or provide information in the Z-axis direction.

[0129] In systems employing multiple lenses and / or cameras, pairing each lens with a different frequency filter, combined with the color separation provided by the image sensor, can further improve image acquisition and accuracy.

[0130] In one example, the internal optical system includes a liquid crystal display (LCD) configured to partially shield a portion of the lens and / or specific frequencies of the received light. This technique enables 3D image processing using a single lens and / or image sensor.

[0131] Noise reduction In some of the exemplary systems disclosed, system operation may generate significant heat that can degrade the performance and / or image quality of one or more components. For example, some system components, such as circuitry and image sensors, may be adversely affected when exposed to heat.

[0132] In the disclosed examples, as shown in Figure 8(D1), the cooling element 96 may be positioned to cool one or more of the printed circuit board (PCB) 94 and / or the image sensor 70. For example, the cooling element 96 may be an active cooler, such as a thermoelectric (e.g., Peltier) cooler, attached to the PCB 94 on which the image sensor 70 (and / or control circuit) is mounted, and / or connected to the circuit associated with the image sensor 70. In some examples, the cooling element 96 is connected to an additional or alternative heat sink.

[0133] The temperature sensor 98 may be configured to measure the temperature in the image sensor 70 and / or the circuit section. The measured values ​​may be used by the temperature control circuit 100 and / or the processing system 32 to control one or more systems (e.g., cooling elements 96) to adjust the cooling of the components.

[0134] Advantageously, the use of the cooling element 96 helps reduce system vibrations, in contrast to cooling systems employing mechanical blowers. Furthermore, the cooling effect on the image sensor 70 helps lower the lens temperature to or near ambient temperature, thereby reducing the mirage effect that may occur in front of the lens. In addition, cooling of the image sensor 70 results in a reduction of dark current and / or associated background noise when not in use.

[0135] In some disclosed examples, system operation may generate significant vibrations that can degrade the performance and / or image quality of one or more components. To mitigate vibrations in the image sensor 70, one or more motion sensors 102 (e.g., one or more of accelerometers, inertial measuring units, vibration sensors, tilt sensors, etc.) may be arranged to monitor and measure vibrations in and / or near the image sensor 70, as shown in Figure 8(D2).

[0136] In some examples, the measured data may be provided to the drive and control system 106 and / or processing system 32 to calculate a compensation coefficient. For example, image data may be correlated with vibration measurements that exceed one or more thresholds. At the correlated data points, a compensation coefficient may be applied to the image data to correct for excessive vibrations (e.g., those exceeding threshold vibration values).

[0137] In additional or alternative examples, measurements from one or more motion sensors 102 may be analyzed by a drive and control system 106 and / or a processing system 32. Based on the analysis, the drive and control system 106 and / or the processing system 32 may generate one or more control signals to instruct adjustments to the position or orientation of the image sensor 70 via one or more actuators 104. For example, the actuators 104 may include piezoelectric actuators having mechanical amplifiers. In some examples, measurement and compensation calculations may be performed in real time during the imaging operation. In some examples, active vibration control may be implemented in conjunction with image data compensation.

[0138] Since vibrations in the imaging sensor 70 can introduce noise, the use of the described actuator 104 to actively mitigate the effects of vibrations on the system is advantageous and helps reduce external vibrations in the imaging sensor 70.

[0139] The method and system can be implemented in hardware, software, and / or a combination of hardware and software. The method and / or system can be implemented centrally in at least one computing system, or distributedly, with different elements distributed across several interconnected computing systems. Any type of computing system or other device adapted to perform the method described herein is suitable. A typical combination of hardware and software may include a general-purpose computing system, along with a program or other code that, when loaded and executed, controls the computing system to perform the method described herein. Another typical embodiment may include an application-specific integrated circuit or chip. Some embodiments may include a non-temporary machine-readable (e.g., computer-readable) medium (e.g., flash drive, optical disk, magnetic storage disk, etc.) that stores one or more lines of machine-executable code, thereby causing a machine to perform a process such as that described herein. As used herein, the term “non-temporary machine-readable medium” includes all types of machine-readable storage media and is defined to exclude propagated signals.

[0140] As used herein, the terms “circuit” and “circuit section” mean physical electronic components (i.e., hardware) and any software and / or firmware ("code") that can constitute the hardware, that the hardware can execute, and / or that can otherwise be associated with the hardware. As used herein, for example, a particular processor and memory may include a first “circuit” when executing one or more first lines of the code, and a second “circuit” when executing one or more second lines of the code. As used herein, “and / or” means any one or more items in the list linked by “and / or”. For example, “x and / or y” means any element in the set of three elements {(x), (y), (x,y)}. In other words, “x and / or y” means “one or both of x and y”. As another example, “x, y and / or z” means any element of the seven-element set {(x), (y), (z), (x,y), (x,z), (y,z), (x,y,z)}. In other words, “x, y and / or z” means “one or more of x, y and z.” As used herein, the term “exemplary” means to serve as an unrestricted example, case, or illustration. As used herein, the term “for example” begins a list of one or more unrestricted examples, cases, or illustrations. As used herein, whenever a circuit section includes the hardware and code (if any) necessary to perform a certain function, the circuit section is “operable” to perform that function, regardless of whether the performance of that function is disabled or not (e.g., by a user-configurable setting, factory trim, etc.).

[0141] While the Method and / or System has been described with reference to certain specific embodiments, those skilled in the art will understand that various modifications and substitutions can be made without departing from the scope of the Method and / or System. In addition, many modifications can be made without departing from the scope of the Disclosure to adapt the teachings of the Disclosure to specific circumstances or materials. For example, the systems, blocks and / or components of the disclosed examples can be combined, divided, rearranged, and / or otherwise modified. Therefore, the Method and / or System is not limited to the specific embodiments disclosed. Rather, the Method and / or System includes all embodiments that fall within the scope of the appended claims, either literally or under the doctrine of equivalents. The inventions disclosed herein include the following: [Aspect 1] A system for correcting errors during the testing process in a video elongation measurement system, wherein the system is A test system for fixing the test sample, An imaging device is positioned to capture an image of the surface of the test sample, One or more sensors configured to measure one or more parameters related to the test sample, A processing system, Receiving image data from the aforementioned imaging device, Receiving sensor data from one or more of the aforementioned sensors, Comparing the aforementioned image data or sensor data with one or more data thresholds, In response to the image data or sensor data exceeding one or more data thresholds, a correction coefficient is calculated based on the image data and sensor data in part. To instruct the adjustment of the imaging device and system components based at least partially on the correction coefficient, A processing system that performs this task, A system that includes these features. [Aspect 2] The system according to embodiment 1, wherein the system component is an active cooler, an actuator, or an imaging device positioning system. [Aspect 3] The system according to embodiment 1, wherein the imaging device is a single view camera. [Aspect 4] The system according to embodiment 1, wherein the imaging device is two or more cameras. [Aspect 5] The system according to embodiment 1, wherein the sensor is an accelerometer, an inertial measuring unit, a temperature sensor, an infrared sensor, a light-emitting diode sensor, an ultrasonic sensor, or a laser sensor. [Aspect 6] The system according to embodiment 1, wherein the one or more parameters include one or more of the shape or position of the marking, the edge position of the test specimen, or the width of the test specimen. [Aspect 7] The system according to embodiment 1, wherein the correction coefficient is expressed in millimeters, inches, or pixels. [Aspect 8] The system according to embodiment 1, wherein the processing system is located on a remote computing platform that communicates with one or more of the test system or the imaging device. [Aspect 9] The system according to embodiment 1, wherein the processing system is integrated with one of the imaging device or the test system. [Aspect 10] A system for correcting errors during the testing process in a video elongation measurement system, wherein the system is A test system for fixing the test sample, An imaging device is positioned to capture an image of the surface of the test sample, One or more motion sensors configured to measure one or more motion parameters related to the video elongation measurement system, One or more actuators for adjusting the position or orientation of the imaging device, A processing system, Receiving image data from the aforementioned imaging device, Receiving sensor data corresponding to vibration measurement values ​​from one or more motion sensors, Comparing the aforementioned image data or sensor data with one or more data thresholds, In response to the sensor data exceeding one or more data thresholds, a correction coefficient is calculated based on the image data and the sensor data in part. Instructing one or more actuators to adjust the position or orientation of the imaging device based at least partially on the correction coefficient, A processing system that performs this task, A system that includes these features. [Aspect 11] The system according to embodiment 10, wherein the one or more motion sensors include an accelerometer, an inertia measurement unit, a vibration sensor, or a tilt sensor. [Aspect 12] The system according to embodiment 10, wherein the one or more motion sensors are positioned near the one or more image sensors to monitor and measure vibrations in the one or more image sensors. [Aspect 13] The system according to embodiment 10, wherein the one or more data thresholds correspond to one or more data threshold vibration values, and the processing system is further operable to correlate image data with vibration measurements that exceed the one or more threshold vibration values. [Aspect 14] The system according to embodiment 13, wherein the processing system is further operable to correct excessive vibration by applying the compensation coefficient to the image data at the correlated data points that exceed the one or more threshold vibration values. [Aspect 15] The system according to embodiment 10, further comprising a drive and control system that receives commands from the processing system in order to control one or more actuators. [Aspect 16] The system according to embodiment 10, wherein the actuator 104 may include a piezoelectric actuator having a mechanical amplifier. [Aspect 17] The system according to embodiment 10, wherein the aforementioned measurement and compensation calculation can be performed in real time during the imaging operation. [Aspect 18] The system according to embodiment 10, which can implement active vibration control in coordination with image data compensation. [Aspect 19] The system according to embodiment 10, wherein the processing system is located on a remote computing platform that communicates with one or more of the test system or the imaging device. [Aspect 20] The system according to embodiment 10, wherein the imaging device comprises two or more imaging devices, each imaging device being operable to capture an image of the surface of the test sample.

Claims

1. A system for correcting errors during the testing process in a video elongation measurement system, wherein the system is A test system for fixing the test sample, An imaging device is positioned to capture an image of the surface of the test sample, One or more sensors configured to measure one or more parameters related to the test sample, A processing system, Receiving image data from the aforementioned imaging device, Receiving sensor data from one or more of the aforementioned sensors, Comparing the aforementioned image data or sensor data with one or more data thresholds, In response to the image data or sensor data exceeding one or more data thresholds, a correction coefficient is calculated based on the image data and sensor data in part. Based at least partially on the correction coefficient, instructions are given to adjust the position or orientation of the imaging device and the system components. A processing system that performs this task, A system that includes these features.

2. A system for correcting errors during the testing process in a video elongation measurement system, wherein the system is A test system for fixing the test sample, An imaging device is positioned to capture an image of the surface of the test sample, One or more motion sensors configured to measure one or more motion parameters related to the video elongation measurement system, One or more actuators for adjusting the position or orientation of the imaging device, A processing system, Receiving image data from the aforementioned imaging device, Receiving sensor data corresponding to vibration measurement values ​​from one or more motion sensors, Comparing the aforementioned image data or sensor data with one or more data thresholds, In response to the sensor data exceeding one or more data thresholds, a correction coefficient is calculated based on the image data and the sensor data in part. Instructing one or more actuators to adjust the position or orientation of the imaging device based at least partially on the correction coefficient, A processing system that performs this task, A system that includes these features.

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