System and method for video extensometer error correction

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 of and priority to U.S. Provisional Application No. 63 / 180,288, entitled "Systems And Methods For Error Correction For Video Extensometers," filed May 27, 2021. The complete subject matter and content of U.S. Provisional Application No. 63 / 180,288 is hereby incorporated by reference in its entirety. [Background technology]

[0002] Camera-based vision systems have been implemented as part of materials testing systems for measuring specimen strain. These systems collect one or more images of the specimen under test. These images are synchronized with other signals of interest for the test (e.g., specimen load, machine actuator / crosshead displacement, etc.). The images of the test specimen can be analyzed to identify and track the location of specific features of the specimen as the test progresses. Changes in the location of such features, such as changes in the relative position of one or more fiducial features of the specimen, allow the local specimen deformation to be calculated, which in turn allows the specimen strain to be calculated.

[0003] Conventional systems employ cameras or other imaging systems to capture images from which to measure properties of a test sample. However, imaging and / or measurement differences between a known reference position and an actual position can lead to distorted readings and inaccurate measurements. Thus, a system that corrects for such errors is desired. Summary of the Invention

[0004] Disclosed herein is a system and method 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 taken in conjunction with the appended claims.

[0005] The benefits and advantages of the present invention will become readily apparent to those of ordinary skill in the art after considering the following detailed description and accompanying drawings. [Brief description of the drawings]

[0006] [Figure 1] FIG. 1 is a block diagram of an exemplary extensometer system according to aspects of the present disclosure.

[0007] [Diagram 2] FIG. 2 illustrates an exemplary test sample measured in the extensometer system of FIG. 1 according to an embodiment of the present disclosure.

[0008] [Diagram 3] FIG. 2 is a block diagram of another view of the exemplary extensometer system of FIG. 1 according to an embodiment of the present disclosure.

[0009] [Figure 4] FIG. 2 is a block diagram of an example implementation of the extensometer system of FIG. 1 according to an aspect of the present disclosure.

[0010] [Figure 5(A1)] FIG. 1 is a block diagram of an exemplary extensometer system according to aspects of the present disclosure. [Figure 5(A2)] FIG. 1 is a block diagram of an exemplary extensometer system according to aspects of the present disclosure. [Figure 5(A3)] FIG. 1 is a block diagram of an exemplary extensometer system according to aspects of the present disclosure. [Figure 5(A4)] FIG. 1 is a block diagram of an exemplary extensometer system according to aspects of the present disclosure. [Figure 5(A5)] FIG. 1 is a block diagram of an exemplary extensometer system according to aspects of the present disclosure.

[0011] [Figure 6(B1)] FIG. 1 is a block diagram of an exemplary extensometer system according to aspects of the present disclosure. [Figure 6(B2)] FIG. 1 is a block diagram of an exemplary extensometer system according to aspects of the present disclosure. [Figure 6(B3)] FIG. 1 is a block diagram of an exemplary extensometer system according to aspects of the present disclosure.

[0012] [Figure 7(C1)] FIG. 1 is a block diagram of an exemplary extensometer system according to aspects of the present disclosure. [Figure 7(C2)] FIG. 1 is a block diagram of an exemplary extensometer system according to aspects of the present disclosure. [Figure 7(C3)] FIG. 1 is a block diagram of an exemplary extensometer system according to aspects of the present disclosure. [Figure 7(C4)] FIG. 1 is a block diagram of an exemplary extensometer system according to aspects of the present disclosure. [Figure 7(C5)] FIG. 1 is a block diagram of an exemplary extensometer system according to aspects of the present disclosure. [Figure 7(C6)] FIG. 1 is a block diagram of an exemplary extensometer system according to aspects of the present disclosure. [Figure 7(C7)] FIG. 1 is a block diagram of an exemplary extensometer system according to aspects of the present disclosure. [Figure 7(C8)] FIG. 1 is a block diagram of an exemplary extensometer system according to aspects of the present disclosure.

[0013] [Figure 8(D1)] FIG. 1 is a block diagram of an exemplary extensometer system according to aspects of the present disclosure. [Figure 8(D2)] FIG. 1 is a block diagram of an exemplary extensometer system according to aspects of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

[0015] 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.

[0016] In particular, the disclosed examples provide systems and methods that address noise in a video extensometer system by employing a fluid delivery system, a vibration control system, and / or saturation control. Additionally, the disclosed examples provide systems and methods that address imaging challenges in a video extensometer system by employing multiple cameras. Additionally, the disclosed examples provide systems and methods that monitor changes in a test specimen by monitoring reference patterns and / or objects in a video extensometer system. Additionally, the disclosed examples provide systems and methods that mitigate thermal and / or external vibration in a video extensometer system by employing compensation techniques that include 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 sample. The errors may be due to limitations of the system components (e.g., physical / operational limitations of the components, operational effects on related components, etc.), system calibration (e.g., for measuring different materials / samples), and / or measurement and / or analytical limitations (e.g., collection and analysis of the 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 a material testing process (e.g., to determine strain of a test specimen). Such systems may capture multiple images of the test specimen and synchronize these images with other information related to the testing process (e.g., specimen load, machine actuator / crosshead displacement, etc.). The images of the test specimen may then be analyzed via one or more algorithms to identify and / or locate specific features of the test specimen (including reference features) and even track such features as the testing operation progresses. Changes in the absolute and / or relative locations of such features allow local specimen deformation to be calculated, which in turn allows specimen strain to be computed.

[0019] The sample features of interest may consist of markings (e.g., fiducial features) applied to the surface of the test sample that are visible to the camera(s). For example, the processor may analyze the image to determine the location and / or shape of the markings (and their changes) and track the movement of these marks relative to one another during testing. For example, multiple markings may be present on the front surface of the sample, such as pair groupings for the determination of gage-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 of interest for the determination of lateral sample strain are the edges of the test sample.

[0020] For single or multiple camera measurement systems, a 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 (e.g., size, position, width, etc.) captured by the imaging device and one or more physical characteristics (e.g., determined in physical coordinates) on the calibration plane.

[0021] Such a calibration process may employ a calibration reference device placed on a calibration plane. The reference device includes predetermined physical characteristics with known geometric dimensions associated with covering some or all of a field of view (FOV) of interest. The calibration process allows an image of the calibration device to be captured and compared to the known calibration device geometry, where a transfer function is established to transform the coordinates of the image 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 locations of markings on the surface of a test specimen. During the testing process, image processing algorithms are executed (e.g., via a processor in the video extensometer system) to determine the locations of the markings on the surface of the specimen. Based on the determined locations, the processor can calculate the initial sample gage length and the instantaneous change in the sample gage length (e.g., axial and / or lateral strain) from the value(s) at the start of the test specimen. The precision with which the video extensometer system can measure the absolute and / or relative positions and / or position changes of the markings depends at least in part on whether the surface of the specimen 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) will result 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) will result in larger measurement errors.

[0023] In some examples, multiple test samples are subjected to a calibration process followed by a testing process, where each sample has a different thickness and / or the thickness of the test samples varies during testing, such that the distance between the surface of the sample and the imaging device varies during the testing process and / or from sample to sample.

[0024] Such perspective errors may be more problematic, for example, in material testing applications where absolute dimensional measurements are desired versus testing applications where measurements are used to determine proportional (e.g., ratiometric) strain. In determining proportional strain, perspective errors result in similar proportional errors in the initial gage length measurement and / or strain displacement measurement. Since strain is calculated as displacement across the gage length, the perspective errors are present in both the numerator and denominator and therefore cancel out. These perspective errors may also affect accuracy at smaller strains, where the perspective errors may dominate the intended strain measurement signal.

[0025] Previous systems have attempted to mitigate some of these problems by various techniques, each with significant drawbacks. One option is for a calibration plane to be placed at the average or mid-plane of all test planes of interest so that perspective measurement errors are optimized across samples with different thicknesses. Another option is to make physical adjustments to the test sample mounting position of the extensometer to maintain a single working distance equal to the calibration plane to compensate for different test samples. Yet another option is to use telecentric optics, which are much less susceptible to out-of-plane perspective errors, but are more expensive and have a more limited field of view. In yet another option, multiple cameras can be employed to capture perspective information from various angles that can be incorporated into the calibration and sample measurement process.

[0026] However, existing solutions for mitigating perspective errors encountered in video extensometer systems all have drawbacks. For example, using an average distance to a calibration plane and / or using less accurate measurement equipment inevitably results in less accurate measurements. Making manual adjustments to the mounting position of the extensometer to compensate for different sample thicknesses is time consuming and requires the operator to remember to consistently make multiple different adjustments for each sample based on its individual thickness. Moreover, such adjustments are difficult to automate.

[0027] Telecentric optics are large, heavy, 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 mitigate systematic and deterministic errors in video extensometers due to changes in Z-axis distance, changes in the measurement plane relative to the calibration plane, errors due to external noise including heat and vibration, as a non-limiting list of examples. In some examples, the errors are corrected in real time during the testing process.

[0029] As described herein, material testing systems, including those that apply tension, compression, and / or torsion, include one or more components that produce displacement and / or load bearing forces to apply stress to and / or measure stress on a test specimen. In some examples, a video extensometer system is employed in sample strain testing that can include one or more of collecting high resolution images, providing the images to an image processor, analyzing the images to identify one or more sample properties that correspond to the displacement or strain values, and generating an output corresponding to the properties.

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

[0031] As disclosed herein, an imaging device employing a camera-based image capture (e.g., vision or video) system is implemented in a materials testing system for the measurement of strain on a test specimen. Such a system collects multiple images of the specimen under test (i.e., during the testing process), which are synchronized with other signals related to the test (e.g., specimen load, machine actuator and / or crosshead displacement, etc.). The specimen images are analyzed by an algorithm (e.g., in real-time and / or after the test) to locate and track specific specimen characteristics as the test progresses. For example, changes in the location, size, shape, etc. of such characteristics allow the deformation of the specimen to be calculated, which in turn leads to the analysis and calculation of the specimen strain.

[0032] Thus, the systems and methods disclosed herein compensate for errors in video extensometer systems including noise, perspective variations, and / or component placement and / or movement.

[0033] In a disclosed example, a system for correcting errors during a testing process in a video extensometry system includes a testing system that holds a test sample, an imaging device positioned to capture images of a surface of the test sample, one or more sensors configured to measure one or more parameters related to the test sample, and a processing system that receives image data from the imaging device, receives sensor data from the one or more sensors, compares the image data or sensor data to one or more data thresholds, calculates a correction factor based in part on the image data and the sensor data in response to the image data or sensor data exceeding the one or more data thresholds, and directs adjustments of the imaging device and system components based at least in part on the correction factor.

[0034] In some examples, the system component is an active cooler, an actuator, or an imaging device position system. In examples, the imaging device is a single view camera. In examples, the imaging device is two or more cameras.

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

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

[0037] In some disclosed examples, a system for correcting errors during a testing process in a video extensometry system includes a testing system that fixes a test sample, an imaging device positioned to capture images of a surface of the test sample, one or more motion sensors configured to measure one or more motion parameters associated with the video extensometry system, one or more actuators that adjust a position or orientation of the imaging device, and a processing system that receives image data from the imaging device, receives sensor data corresponding to vibration measurements from the one or more motion sensors, compares the image data or sensor data to one or more data thresholds, and in response to the sensor data exceeding the one or more data thresholds, calculates a correction factor based in part on the image data and the sensor data, and instructs the one or more actuators to adjust the position or orientation of the imaging device based at least in part on the correction factor.

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

[0039] In some examples, the one or more data thresholds correspond to one or more data threshold vibration values, and the processing system is further operable to correlate the image data with vibration measurements that exceed the one or more threshold vibration values. In examples, the processing system is further operable to apply a compensation factor to the image data at correlated data points that exceed the one or more threshold vibration values ​​to correct for excess vibration.

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

[0041] In some examples, measurements and compensation calculations can be performed in real-time during the imaging operation. In 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 in communication with one or more of the test system or the imaging device, in examples, the imaging device includes two or more imaging devices, each imaging device operable to capture an image of a surface of the test specimen.

[0043] Referring now to the figures, Figure 1 illustrates an exemplary extensometer system 10 for measuring changes in one or more properties of a test specimen 16 undergoing mechanical property testing. The exemplary extensometer system 10 may be connected to a testing system 33 capable of mechanically testing the test specimen 16, for example. The extensometer system 10 may measure and / or calculate changes in the test specimen 16 undergoing, for example, compressive strength testing, tensile strength testing, shear strength testing, bending strength testing, flexure strength testing, tear strength testing, peel strength testing (e.g., adhesive bond strength), torsional strength testing, and / or any other compressive and / or tensile testing. Additionally or alternatively, the material extensometer system 10 may perform dynamic testing.

[0044] According to a disclosed example, the extensometer system 10 may include a testing system 33 that manipulates and tests the test specimen 16, and / or a computing or processing system 32 communicatively coupled to the testing system 33, a light source, and / or an imaging device, as further shown in Figure 4. The testing system 33 applies a load to the test specimen 16 and measures 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 back screen 18 that illuminates the test specimen 16. The extensometer system 10 includes a processing system 32 (see also FIG. 4) and a camera or imaging device 12. While the example of FIG. 1 shows a single camera 12, the disclosed examples are applicable to a multiple camera extensometer system 10. In some examples, the light source 14 and imaging device 12 are configured to transmit and receive in infrared (IR) wavelengths, although other illumination sources and / or wavelengths are applicable as well. In some examples, one or both of the light source 14 or imaging device 12 include one or more filters (e.g., polarizing filters), one or more lenses. In some examples, a calibration routine (e.g., a two-dimensional calibration routine) is performed to identify one or more characteristics of the test specimen 16, one or more markers 20 (including a pattern of markers), and / or other characteristics of the test specimen 16, and / or other characteristics of the markers 20 (including a pattern of markers), ...

[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 receive measurement data (e.g., transducer measurements such as force and displacement) and / or test results (e.g., peak force, break displacement, etc.) from the test system 33 for processing, displaying, reporting, and / or any other desired purpose. The extensometer system 10 connects to the test system 33 and software utilizing standard interfaces including Ethernet, analog encoder or SPI. This allows the device to be plugged into and used by existing systems without the need for dedicated integrated software or hardware. The extensometer system 10 provides real-time axial and lateral encoder information or analog information to the materials testing machine 33. The real-time video extensometer 10 and materials testing machine 33 exchange real-time test data, including extension / strain data, with the external computer 32, which can be configured, via wired and / or wireless communication channels. The extensometer system 10 provides measurements and / or calculations of stretch / strain data captured from a test specimen 16 undergoing testing in a materials testing machine 33 and further provides the stress data and stretch / strain data to a processor 32 .

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

[0048] FIG. 2 is an exemplary test specimen 16 that may be measured in the extensometer system 10 of FIG. 1. For example, one or more markings 20 (e.g., fiducial features) are affixed to a surface 28 that faces the light source 14 and imaging device 12. The grippers 26 are configured to be placed in grips of a test system 33 (see also FIG. 4) to apply a force to the test specimen 16. For example, a cross-member loader applies a force to the test specimen 16 while the grippers are gripping or otherwise coupled to the test system 33. A force applicator, such as a motor, moves the crosshead relative to the frame to apply a force to the test specimen 16 as indicated by a double-headed arrow 34. The force 34 that pulls the grippers 26 apart may stretch the test specimen 16, causing the markings to move from a first position 20A to a second position 20B. Additionally or alternatively, the markings may change shape or size, which can also be measured by the processing system 32 by viewing the captured images. The force 34 may also move the edge of the test specimen from the first position 22A to the second position 22B. For example, in the first or initial position, the edge has a width 24A and decreases to width 24B upon application of the force 34.

[0049] Based on the captured images, the processing system 32 is configured to perform stretching / straining in the measurement process. For example, to detect stretching / straining in the test specimen 16, the processing system 32 monitors the images provided via the imaging device 12. When the processing system 32 identifies a change in the relative position between two or more of the markers and / or edges of the test specimen 16 (e.g., compared to an initial location at the beginning of the crosshead movement), the processing system 32 measures the amount of change and calculates 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 the light to create a dark silhouette for edge analysis.

[0050] As disclosed herein, the video extensometer system 10 is configured to make optical width measurements of an opaque test specimen 16. The imaging device 12 is positioned to view a surface 28 of the test specimen 16 that faces the imaging device 12. The surface 28 is located near the focal plane of the imaging device optics (see, e.g., FIG. 3).

[0051] As shown in Figure 3, the video extensometer system 10 is arranged to measure one or both of axial strain (based on changes in the markers 20 and / or marker pattern on the front surface 28 of the test specimen 16) and transverse 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 in 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 physical testing (e.g., at regular intervals, continuously, and / or based on one or more thresholds associated with time, force, or other suitable test characteristic).

[0052] As shown, 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 variable during the testing process.

[0053] The test specimen 16 is characterized by suitable marks or fiducial features 20 on a forward facing surface 28 (and / or an opposing surface) of the test specimen 16. Analysis of one or more images associated with the video extensometer system 10 is performed via a processing system 32 to implement identification algorithms that allow both the markings 20 of the test specimen 16 and the edges 22 of the test specimen to be continuously tracked and measured during the testing process.

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

[0055] In some examples, the measurements and / or positions of the one or more edges are provided in pixel coordinates captured by imaging device 12. Additionally or alternatively, the measurements and / or positions of the 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 positioning and / or dimensions of the test specimen within the test system prior to measurements, and similar coordinate systems / units may be employed during the testing process.

[0056] FIG. 4 is a block diagram of an exemplary extensometer system 10 of FIG. 1. As shown in FIG. 1, the extensometer system 10 includes a test system 33 and a computing device 32. The exemplary 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 of FIG. 4 includes 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 devices, such as an FPGA, a RISC processor with an ARM core, an image processing device, a digital signal processor, and / or a system on a chip (SoC). The processor 202 executes machine-readable instructions 204, which can be stored locally at the processor (e.g., in an internal cache or on the SoC), in a random access memory 206 (or other volatile memory), in a read-only memory 208 (or other non-volatile memory, such as a flash memory), and / or in a mass storage device 210. The exemplary mass storage device 210 may be a hard drive, a solid state storage drive, a hybrid drive, a RAID array, and / or any other mass data storage device. The bus 212 enables communication between the processor 202, the RAM 206, the ROM 208, the mass storage device 210, the network interface 214, and / or the input / output interface 216.

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

[0058] The example I / O interface 216 of FIG. 4 includes hardware, firmware, and / or software that couples one or more input / output devices 220 to the processor 202 to provide input to and / or output from the processor 202. For example, the I / O interface 216 can include an image processing device that interfaces with a display device, a Universal Serial Bus port that interfaces with one or more USB-compliant devices, FireWire, Fieldbus, and / or any other type of interface. The example extensometer system 10 includes a display device 224 (e.g., an LCD screen) coupled to the I / O interface 216. Other example I / O device(s) 220 can include a keyboard, a keypad, a mouse, a trackball, a pointing device, a microphone, an audio speaker, a display device, an optical media drive, a multi-touch touch screen, a gesture recognition interface, a magnetic media drive, and / or any other type of input and / or output device.

[0059] Computing device 32 may access non-transitory machine-readable medium 222 via I / O interface 216 and / or I / O device(s) 220. Examples of machine-readable medium 222 in Figure 4 include optical disks (e.g., compact disks (CDs), digital versatile / video disks (DVDs), Blu-ray® disks, 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 medium.

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

[0061] The test system 33 includes a frame 228, a load cell 230, a displacement transducer 232, a cross-member loader 234, a material fixture 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 tests. The load cell 230 measures the force applied by the cross-member loader 234 to the material under test via grips 248. The cross-member loader 234 applies the force to the material under test while the material fixture 236 (also referred to as a grip) grips or otherwise couples the material under test to the cross-member loader 234. An exemplary cross-member loader 234 includes a motor 242 (or other actuator) and a crosshead 244. As used herein, "crosshead" refers to the component of a materials testing system that applies directional (axial) and / or rotational forces to a sample. The materials testing system may have one or more crossheads, and the crosshead(s) may be positioned in any suitable position and / or orientation in the materials testing system. The crosshead 244 couples 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 forces to the material under test. Exemplary actuators that may be used to provide forces and / or movements of the components of the extensometer system 10 include electric motors, pneumatic actuators, hydraulic actuators, piezoelectric actuators, relays, and / or switches.

[0062] Although the example test system 33 uses a motor 242, such as a servo motor or a direct drive linear motor, other systems may use different types of actuators, for example hydraulic actuators, pneumatic actuators, and / or any other type of actuator based on the requirements of the system.

[0063] Exemplary grips 236 include compression platens, jaws, or other types of fixtures depending on the mechanical property and / or material being tested. Grips 236 can be manually configured, controlled via manual input, and / or automatically controlled by control processor 238. Crosshead 244 and grips 236 are operator accessible components.

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

[0065] The example control processor 238 communicates with the computing device 32, e.g., receives test parameters from the computing device 32 and / or reports 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 may control the cross member loader 234 to increase or decrease the applied force, may control the fixture(s) 236 to grip or release the material under test, and / or may receive measurements from the displacement transducers 232, the load cells 230, and / or other transducers.

[0066] The exemplary control processor 238 is configured to perform the stretch / strain measurement process as the test specimen 16 is undergoing testing in the testing 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. When the control processor 238 identifies a change in location and / or position of the edge 22 of the test specimen 16 (e.g., compared to an initial location at the beginning of the crosshead 244 movement), 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 over several images to calculate the stretch / strain in real time. Stress and strain data is exchanged between the real-time video extensometer 10, the testing system 33 and the processing system 32 and is typically organized and displayed via the display device 224.

[0067] Fluid delivery, vibration control, vibration compensation, saturation control fluid delivery Some example systems operate in an environment that includes an imaging device or camera, lighting, a test platform, and a sample. Operation of the system generates heat and / or receives heat from the environment, which may result in temperature differences in the test environment. For example, a thermal boundary layer may form in the air near the lens (and / or at one or more locations between the lens and the test sample), resulting in air having a varying density in the area immediately in front of the lens (and / or at one or more locations between the lens and the test sample). This in turn increases the likelihood 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 FIG. 5(A1), a directional gas outlet 64 (e.g., a fluid ejection device, air / gas nozzle, air / gas knife, etc.) provides one or more gases / fluids from a fluid source 62 to replace, reduce, condition, and / or flow the air and the resulting thermal boundary layers (hot and / or cold). In this manner, the gas outlet 64 can mix the air in front of the lens with the ambient air in the test area. As a result, air density variations (e.g., temperature differences) are reduced, reducing the possibility of mirage errors.

[0069] In some instances, the employment of gas outlet 64 can increase the frequencies associated with noise observed during imaging operations by moving and / or mixing air (at or near the lens, between the lens and the test sample, etc.), which allows for facilitated filtering (e.g., via digital filters, software and / or hardware filters, etc.) to remove the effects of noise from processed data (e.g., imaging measurements, etc.).

[0070] In some instances, the employment of gas outlets 64 helps to redistribute air of various temperatures, including by applying gas / air at a temperature within a threshold amount of the ambient temperature. Additionally, the application of gas / air can wash dust, particulates, condensation, and / or other objects (e.g., bugs) off the lens that may adhere to the lens.

[0071] Active Vibration Control-A Some example systems operate in an environment that includes cooling fans as well as external sources that can affect the operation of system components such as the camera, lighting, test platform, and sample. The operation of the system can result in vibrations that can result in movement of the image sensor / lens relative to the sample during the testing process, which in turn results in noisy image data. It is the vibration modes that do not produce common mode effects that have a significant impact on the test data.

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

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

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

[0075] Active Vibration Control-B 5(A3), the PCB 66 on which the image sensor 70 is mounted is itself mounted to another substrate 72 or to a housing wall. An active vibration cancellation unit 68 may be mounted to the PCB 66, to the PCB mounting fasteners 74, and / or between the PCB 66 and the PCB mounting fasteners 74. According to a disclosed example, the active vibration cancellation unit 68 may be responsive to vibrations (e.g., based on motion sensor feedback, etc.) to mitigate vibrations local to the image sensor 70 and reduce induced noise on the image signal.

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

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

[0078] lighting compensation Some exemplary systems are configured to optimize the illumination provided to the system to meet certain illumination criteria, such as providing sufficient illumination to the test sample to reduce 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. These limitations can result in reduced image precision and accuracy due to non-optimal lighting conditions, including, for example, image saturation.

[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 a test operation, as shown in FIG. 5(A5). As the image operation proceeds, the image data is analyzed (e.g., at the camera 12 via the image sensor 70, at the processing system 32, etc.) and compared to one or more thresholds (e.g., light intensity in the image, etc.). In response to exceeding a threshold, the intensity of the light source 14A may be automatically adjusted to provide a desired level of image saturation for the sample under test. In some examples, one or more light-sensitive sensors are employed to measure light intensity (e.g., at the test sample 16, at the camera 12, etc.), which may be employed by the processing system 32 to determine adjustment values ​​for the light source.

[0081] Z-axis movement 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 dimension of the test sample. For video extensometers that measure displacement between multiple reference features (e.g., 2 points, 4 points, etc.), a change in the Z-axis position of the sample will cause a change in the imaged dimension between the reference points that are not related to the test motion, leading to errors in the strain measurement, for example. However, the disclosed examples provide methods and systems that reduce and / or eliminate errors associated with Z-axis errors.

[0082] Telecentric Lenses In some example systems, the video extensometer system employs one or more conventional optical lenses with an angular field of view. As a result, imaging suffers from parallax error, which can increase or decrease the magnification of the test sample being measured as the object moves toward or away from the lens. In the disclosed examples, the video extensometer system employs one or more telecentric lenses, which reduces 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, where at least one camera is fitted with a telecentric lens having a relatively small field of view (50 mm to 90 mm, e.g., camera 12B in FIG. 7(C6)), and another camera(s) (e.g., camera 12 as shown in the figures) employs an entcentric lens with a different field of view, which may be larger than the field of view associated with the telecentric lens.

[0084] During imaging operations, errors caused by Z-axis motion of the test sample are generally more pronounced in the early stages of the testing process (as opposed 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 Z-axis motion. As strain applied to the test sample increases (e.g., beyond a threshold amount of strain, test sample expansion, time, and / or in response to a prompt), the system transitions to analysis using measurements acquired with the remaining cameras employing entocentric lenses.

[0085] Projected Pattern In some examples, the video extensometer system may measure Z-axis motion by analyzing changes associated with features of the test sample that are independent of the deformation of the test sample. For example, an image or other feature may be projected onto the surface of the test sample. For example, a laser 78 and / or other type of projector may project a feature (e.g., a dot, line, pattern, etc.) as shown in FIG. 6(B1). The lens 15 and image sensor 70 can measure Z-axis motion by measuring the change and / or displacement of the projected feature, such as by using a known angle α between the projected light and the surface of the test sample.

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

[0087] Thus, the camera 12, image sensor 70, and processing system 32 may be utilized to both track the reference features of the test specimen and to process measurements of the projected features / patterns. The changes in the projected features may be calculated in the processing system 32 to determine the amount of Z-axis movement. Error correction values ​​may then be calculated and / or determined (e.g., by looking up a list of corresponding Z-axis changes for compensation factors) and applied to the measurements associated with each change to the reference feature(s) to improve the test results.

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

[0089] Laser Triangulation Sensor In some disclosed examples, as shown in FIG. 6(B2), laser triangulation sensor(s) 88 are employed to detect Z-axis motion of the test sample 16. For example, a laser source 82 may generate a laser light 87 that is directed toward the test sample 16 via one or more lenses 84. The reflected laser light 87A is received at a light receiving element 88 (e.g., a photosensitive sensor) that is configured to generate a signal proportional to the magnitude of Z-axis motion. The signal from the light receiving element 88 is analyzed in the processing system 32 to calculate a compensation factor to correct for the motion.

[0090] For example, one or more characteristics of the received light (eg, phase, intensity, frequency, etc.) may be correlated to changes in the Z axis direction.

[0091] Reference Object In some example systems, as shown in FIG. 6(B3), a reference scale object 90 may be positioned with and / or near the test sample 16. When the test sample 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 sample 16 and / or the reference scale object 90 during the imaging operation.

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

[0093] Multiple Cameras As a tool that provides highly accurate results, video extensometry systems face several challenges. Generally, strain calculations during extensometry testing assume that the measurements taken during testing are accurate measurements of the (often two-dimensional) changes to the test specimen. These dimensions are how the shape of the specimen changes in the axial dimension (X-axis) and lateral dimension (Y-axis). In video extensometry, a camera provides two-dimensional measurements by positioning and focusing the camera on the surface of the test specimen to directly observe the changes in the specimen in the X-axis and Y-axis.

[0094] However, in a real test environment, there may be variations in the Z axis (eg, the distance from the camera to the test sample) that can affect the accuracy of the measurements in the X and Y directions.

[0095] For example, as shown in FIG. 7C7, if the test specimen 16 is set up before the start of the test, it may be positioned at a different Z distance 39B from the camera 12 than the Z distance 39A at which the camera 12 was last calibrated. If the test specimen is set up before the start of the test, it may be set up so that it is not perfectly vertical, so that the top 97A and bottom 97B of the test specimen are at different Z distances 39C, 39D from the camera, as shown in FIG. 7C8. At the start of the test, the test specimen itself may not have a consistent Z distance / shape across its face. For example, it may have a bowed center due to how it was made or due to physical stresses induced during the test setup. When the tracked fiducial feature moves in the Z direction relative to the calibration plane, this may result in perspective errors (e.g., phantom distortion). Such perspective errors can dominate the true data, especially in regions of low strain (eg, elastic regions of relatively stiff materials), and lead to inaccurate measurements.

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

[0097] In some instances, there is a balance between accuracy and field of view in a video extensometry system. The camera used for video extensometry has a certain inherent image resolution. This resolution will affect the accuracy of the two-dimensional measurements calculated from the image. The lenses used in the system can map this inherent image resolution to the field of view of the test space. Using magnification to increase the size of the field of view is a trade-off with reduced accuracy.

[0098] As the test progresses, some samples may change significantly enough during the test (e.g., increase in one or more dimensions) that at the end of the test, the field of view required to cover the entire sample may be significantly larger than the field of view at the start of the test. This is typical for so-called "highly elongated" materials, but can be true for a variety of materials.

[0099] Some material tests have a greater need for precision at the beginning of the test than at the end of the test, however the need for a video extensometry system to cover a larger field of view throughout the test limits the measurement precision available compared to being able to focus on a smaller field of view at the beginning of the test.

[0100] To address these and other sources of error, various system configurations have been disclosed, such as employing multiple cameras to measure distortion.

[0101] In some examples, the multi-camera video extensometry system 10 employs one or more forward-facing cameras and one or more side-facing cameras. In the example of Figure 7(C1), a forward-facing camera 12 is used to measure changes in a reference feature 20 as disclosed herein. A side-facing camera 12A is added to track changes in the Z-axis 39 between the camera 12 and the test specimen 16.

[0102] In this manner, the side-looking camera 12A can track the movement of the test specimen 16 toward or away from the front-looking camera 12. The test specimen 16 can be tilted during a testing operation such that a first or upper portion and a second or lower portion of the test specimen have different Z distances from the front-looking camera 12 (e.g., a constant or changing angle during testing) but maintain a linear relationship between the first and second portions.

[0103] In some instances, the shape of the test specimen 16 may change in the Z-axis 39 direction during testing, such that the shape of the specimen may curl inward or outward during testing, and / or may start out in a curled state but straighten out during testing.

[0104] In some examples, side-facing measurements may be used to enable and / or disable accuracy of pre-test and / or post-test operations, provide interactive information to the operator while loading the specimen to guide installation, correct (or compensate) position data generated by the forward-facing camera after the test is completed, correct (or compensate) position data generated by the forward-facing camera in real-time during the test operation, and / or may be configured with front or rear lighting schemes for direct or silhouette image capture, as a non-limiting list of examples. For example, measurements from both the forward-facing camera 12 and the rear-facing camera 12A may be provided to processing circuitry 32 to measure distortion of test specimen 16.

[0105] In some examples, as shown in the example of FIG. 7(C2), the video extensometry system 10 employs two or more forward-facing cameras 12, 12B with similar imaging and / or sensing capabilities, each mounted at a slightly offset angle from one another, but with the same field of view (e.g., of the test sample 16). Although FIG. 7(C2) illustrates employing two cameras, additional cameras (e.g., three, four or more) may be included. For example, each camera may be positioned at different distances, at different angles relative to another camera and / or the test sample, and / or may have different optical properties (e.g., focus, magnification, optical power, etc.).

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

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

[0108] In some examples, as shown in FIG. 7(C6), two forward-facing cameras 12, 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 contained within the field of view of the first camera.

[0109] The first, wide field of view camera is configured to encompass the full range of motion of the specimen during the test operation, but has a lower resolution compared to the second camera, and thus the second, narrow field of view camera provides a relatively high resolution view of a gage length portion of the specimen at the start of the test, some of which may move out of the field of view of this camera during the test.

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

[0111] Additionally, comparing the overlap of each field of view provides Z-axis compensation, e.g., the overlap and associated Z-axis compensation provides high resolution imaging of the portion of Z-axis movement during initial sample loading and testing.

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

[0113] In this example, the use of a first field of view and a second field of view nearly doubles the amount of distance that a highly extensible sample can be measured.

[0114] Additionally, the overlap of the two fields of view between the two cameras provides an amount of stereo vision, facilitating Z-axis measurement, and therefore correction, as disclosed herein. The overlap provides information about Z-axis motion, which allows for measurement and / or correction during loading and / or testing of the test specimen.

[0115] In some instances, larger extensional displacement samples can be covered by extending the system to more cameras (e.g., three, four or more cameras) with some overlap between adjacent fields of view.

[0116] In some disclosed examples, as shown in the example of 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 prompted by an operator (e.g., in response to sensor measurements) and can be incorporated during a calibration step and / or in real-time during the testing process.

[0117] Adjustments to the field of view may include one or more of changes to camera magnification, camera or lens position and / or orientation (eg, vertical and / or horizontal positioning), as a non-limiting list of examples.

[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 provided by the camera or associated optics) in order to track with high precision the position of the reference feature as it moves and / or changes during testing.

[0119] Depending on the type of sample and / or material testing needs, 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 example 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, a first camera has a first field of view covering a first or upper portion of the test space (or test sample) and a second camera has a second field of view covering a second or lower portion of the test space (or test sample), with no overlap between the first and second fields of view.

[0121] In some examples, the first and second cameras will each be focused on a unique first or second reference feature 21A or 21B, respectively, such that 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 the other reference features (within the limitations of the test space and calibration procedure) at the start of the calibration phase and / or testing process. This provides high resolution and accuracy for test samples that have relatively large initial gage lengths. The measurements from each camera may then be provided to a processor (e.g., processing system 32) for analysis that takes into account the predetermined relationship 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 expense of employing multiple sets of lenses, cameras, and / or image sensors.

[0124] 7(C4), one or more external mirrors 92 are positioned around the test sample 16 and / or camera 12. The one or more mirrors 92 are positioned such that a single camera 12 can view the test sample 12 and mirrors 92 without moving and / or adjusting the focus or position of the camera 12. Thus, the test sample 12 and / or mirrors 92 can be viewed simultaneously (and / or periodically, alternating, and / or in response to prompting).

[0125] 7(C5), multiple mirrors 92A and 92B may be used to view the test sample 12 from various angles (e.g., the side of the test sample, the opposite side of the test sample, etc.) The mirror(s) 92 may be designed to provide the same level of magnification as a direct view of the test sample 16 by the camera 12, or may be designed to provide a different level of magnification.

[0126] In some examples, one or more internal optics 93 (e.g., prisms, mirrors, gratings, filters, etc.) may be employed (e.g., within camera 12, test system 33, etc.) to manipulate the received light. Rather than employing a dedicated image sensor for each lens, light from multiple lenses or other optics (e.g., mirrors) may be directed to different portions of a single image sensor 70. In some examples, light received from a single lens 15 may be replicated (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 redirected onto different portions of the same image sensor and / or onto different image sensors to improve noise and / or provide Z-axis information, taking into account one or more properties of the light (e.g., phase, frequency, etc. of the light. For example, by employing multiple lenses, a different frequency filter can be used for each lens. This information can then be combined with other light data, such as the spatial frequency separation provided by a prism.

[0128] In some examples, 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 a test sample using multiple frequencies of light simultaneously. Advantageously, employing a color image sensor instead of a monochrome sensor simplifies the calibration process, reduces costs, and simplifies reference feature tracking algorithms. The use of a sensor configured to receive multiple frequencies of light can further improve noise reduction and / or provide Z-axis information.

[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 collection and accuracy.

[0130] In one example, the internal optics includes a portion of a lens and / or a liquid crystal display (LCD) configured to partially block certain frequencies of received light. This approach allows for 3D imaging using a single lens and / or image sensor.

[0131] Noise Reduction In some disclosed example systems, system operation can 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, image sensors, etc., can be adversely affected when exposed to heat.

[0132] In disclosed examples, as shown in FIG. 8(D1), a cooling element 96 may be positioned to cool one or more of a printed circuit board (PCB) 94 and / or 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 circuitry) is mounted and / or in communication with circuitry associated with the image sensor 70. In some examples, the cooling element 96 is connected to an additional or alternative heat sink.

[0133] Temperature sensor 98 may be configured to measure the temperature at image sensor 70 and / or the circuitry. The measurements may be used by temperature control circuit 100 and / or processing system 32 to control one or more systems (e.g., cooling element 96) to regulate cooling of the components.

[0134] Advantageously, the use of cooling element 96 helps to reduce vibrations in the system as opposed to cooling systems that employ mechanical fans. Additionally, the cooling effect on image sensor 70 helps to reduce the lens temperature to or near ambient temperature, thereby reducing the mirage effect that may occur in front of the lens. Additionally, cooling image sensor 70 results in a reduction in dark current and / or associated background noise, such as when not in use.

[0135] In some disclosed examples, system operations may generate significant vibrations that may 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 an accelerometer, an inertial measurement unit, a vibration sensor, a tilt sensor, etc.) may be positioned to monitor and measure vibrations in and / or near the image sensor 70, as shown in FIG. 8(D2).

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

[0137] In additional or alternative examples, measurements from one or more motion sensors 102 may be analyzed by the drive and control system 106 and / or the 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 direct 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 with mechanical amplifiers. In some examples, measurements and compensation calculations may be performed in real-time during the imaging operation. In some examples, active vibration control may be implemented in coordination with image data compensation.

[0138] Since vibrations in the imaging sensor 70 can introduce noise, the described use of the actuator 104 to actively mitigate the effects of vibrations on the system advantageously helps to mitigate 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 in a centralized manner in at least one computing system, or in a distributed manner where different elements are distributed across several interconnected computing systems. Any kind of computing system or other device adapted to perform the methods described herein is suitable. A typical combination of hardware and software can include a general-purpose computing system, with a program or other code that, when loaded and executed, controls the computing system to perform the methods described herein. Another typical embodiment can include an application specific integrated circuit or chip. Some embodiments can include a non-transitory machine-readable (e.g., computer-readable) medium (e.g., flash drive, optical disk, magnetic storage disk, etc.), which stores one or more lines of code executable by a machine, thereby causing the machine to perform a process as described herein. As used herein, the term "non-transitory machine-readable medium" is defined to include all types of machine-readable storage media and to exclude propagating signals.

[0140] As used herein, the terms "circuitry" and "circuitry" refer to physical electronic components (i.e., hardware) and any software and / or firmware ("code") that may comprise, be executed by, and / or otherwise be associated with hardware. As used herein, for example, a particular processor and memory may include a first "circuitry" when executing a first one or more lines of code, and may include a second "circuitry" when executing a second one or more lines of code. As used herein, "and / or" refers to any one or more of the items in a list linked by "and / or". As an example, "x and / or y" refers to any element of the triplet {(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 serving as a non-limiting example, instance, or illustration. As used herein, the term "for example" begins a list of one or more non-limiting examples, instances, or illustrations. As used herein, whenever circuitry includes the necessary hardware and code (if either is necessary) to perform a function, the circuitry is "operable" to perform that function, regardless of whether implementation of that function is disabled or enabled (e.g., by a user-configurable setting, factory trim, etc.).

[0141] Although the method and / or system have been described with reference to certain specific embodiments, those skilled in the art will recognize that various modifications and equivalents may be substituted without departing from the scope of the method and / or system. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the disclosure without departing from the scope of the disclosure. For example, the systems, blocks and / or components of the disclosed examples may be combined, divided, rearranged, and / or otherwise modified. Thus, the method and / or system is not limited to the particular embodiments disclosed. Instead, the method and / or system includes all embodiments falling within the scope of the appended claims, both literally and under the doctrine of equivalents.

Claims

1. 1. A system for correcting errors during a testing process in a video extensometry system, the system comprising: a test system for mounting a test sample; 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 associated with the test sample; 1. A processing system comprising: receiving image data from the imaging device; receiving sensor data from the one or more sensors; comparing the image data or the sensor data to one or more data thresholds; in response to the image data or the sensor data exceeding the one or more data thresholds, calculating a correction factor based in part on the image data and the sensor data; directing an adjustment of a position or orientation of the imaging device and an adjustment of a system component based at least in part on the correction factor; a processing system for performing the steps of: A system comprising:

2. 1. A system for correcting errors during a testing process in a video extensometry system, the system comprising: a test system for mounting a test sample; 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 associated with the video extensometry system; one or more actuators for adjusting a position or orientation of the imaging device; 1. A processing system comprising: receiving image data from the imaging device; receiving sensor data corresponding to vibration measurements from the one or more motion sensors; comparing the image data or the sensor data to one or more data thresholds; responsive to the sensor data exceeding the one or more data thresholds, calculating a correction factor based in part on the image data and the sensor data; directing the one or more actuators to adjust the position or the orientation of the imaging device based at least in part on the correction factor; a processing system for performing the steps of: A system comprising:

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