A method includes receiving data characterizing a first acoustic signal reflected by a first defect in a target object, and a first depth of the first defect relative to a surface of the target object. The first acoustic signal is detected by a detector located at a first location on the surface of the target object. The method also includes assigning a defect color to the received data based on an amplitude value associated with the first acoustic signal and one or more of a first predetermined threshold value and a second predetermined threshold value associated with the first depth. The method further includes rendering, in a graphical user interface display space, a first visual representation of the first acoustic signal in a graph including a first axis indicative of target object defect depth and a second axis indicative of amplitudes of acoustic signals detected by the detector. The first visual representation of the first acoustic signal includes the assigned defect color.

System include an ultrasonic transducer configured to couple to a nondestructive testing (NDT) sample and configured to produce and direct an ultrasonic probe wave at a selected frequency into a subsurface region of the NDT sample, a 3D laser scanning vibrometer configured to direct a detection beam in a scan area on a surface of the NDT sample and to receive a return beam from the scan area, and to detect, based on the return beam, a 3D motion of the surface across a wideband frequency range, and a processor, and a memory configured with instructions that, when executed by the processor, cause the processor to produce sub-surface image data of the NDT sample at multiple harmonics of the selected frequency in the wideband frequency range based on the detected 3D surface motion, wherein the sub-surface image data describes a nonlinear defect response produced in the NDT sample by interaction of the ultrasonic probe wave with the subsurface region.

According to one embodiment, a control method includes setting a transmission angle of an ultrasonic wave to a standard angle. The control method further includes transmitting an ultrasonic wave at the set transmission angle and detecting an intensity of a reflected wave from an object. The control method further includes calculating a tilt angle based on a gradient of the intensity. The tilt angle indicates a tilt of the object. The control method further includes resetting the transmission angle based on the tilt angle.

Methods and devices are provided for analyzing a tubular structure including at least two electromagnetic-acoustic transducers (EMAT) and, called sensors, attachable or attached in, on or in the vicinity of the tubular structure; and computation and/or memory resources, that are accessed locally and/or remotely and that are configured to determine, for the pair of sensors, a function representing the impulse response of the tubular structure on the basis of the diffuse acousto-elastic noise present in the structure. Developments describe the use of rings supporting the sensors; translation and/or rotation movements; permanent or temporary installations; hinged rings; various computation modes, e.g., intercorrelation, a passive inverse filter, or correlation of the coda of the correlation; the use of artificial noise sources, imaging (e.g., tomography) for determining the existence of one or more defects in the structure. Software aspects are described.

Systems and methods are disclosed for conducting an ultrasonic-based inspection. The systems and methods perform operations comprising: receiving a plurality of scan plan parameters associated with generating an image of at least one flaw within a specimen based on acoustic echo data obtained using full matrix capture (FMC); applying the plurality of scan plan parameters to an acoustic model, the acoustic model configured to determine a two-way pressure response of a plurality of inspection modes based on specular reflection and diffraction phenomena; generating, by the acoustic model based on the plurality of scan plan parameters, an acoustic region of influence (AROI) comprising an acoustic amplitude sensitivity map for a first inspection mode amongst the plurality of inspection modes; and generating, for display, a first image comprising the AROI associated with the first inspection mode for capturing or inspecting the image of the at least one flaw.

The present disclosure provides a system and method for real-time visualization of a material during ultrasonic non-destructive testing. The system includes a graphical user interface (GUI) capable of showing a three-dimensional (3-D) image of a composite laminate constructed of a series of two-dimensional (2-D) cross sections. The GUI is capable of displaying the 3-D image as each additional 2-D cross section is scanned by an ultrasonic testing apparatus in real time or near real time, including probable defect regions that contain a flaw such as a hole, crack, wrinkle, or foreign object within the composite. Furthermore, in one embodiment, the system includes an artificial intelligence capable of highlighting defect areas within the 3-D image in real time or near real time and providing data regarding each defect area, such as the depth, size, and/or type of each defect.

In some implementations, a system may receive a cable map for a deployed cable. The system may receive vibration data indicating a vibration associated with a first section of the cable. The system may determine a characteristic associated with the first section of the cable based on the vibration. The system may determine a location associated with the characteristic based on the cable map. The system may determine that the first section of the cable is associated with the location based on the location being associated with the characteristic. The system may associate the location and a length of a second section of the cable extending from an initial location to the location. The system may receive an input identifying the length of the second section of the cable and may output the location based on associating the location and the length of the second section of the cable.

According to one embodiment, a processing system sets a detector to a prescribed position. The detector includes a plurality of detection elements arranged along a first direction and a second direction. The second direction crosses the first direction. The processing system causes the detector to perform a probe of a weld portion of a joined body. The probe includes a transmission of an ultrasonic wave and a detection of a reflected wave. The processing system calculates a center position of the weld portion in a first plane along the first and second directions based on intensity data. The intensity data is of an intensity of the reflected wave obtained by the probe. The processing system performs a position adjustment of moving the detector along the first plane to reduce a distance between the center position and a position of the detector in the first plane.

A method of fabricating and controlling a thick-film transducer array for steering and focusing ultrasonic waves within a substrate volume is provided. A ceramic film composition can be coated on a substrate volume in one or more layers. The ceramic film can be masked with a plastic sheet out of which an electrode pattern is cut. Conductive electrode material can be applied to the pattern to create a transducer array that can be polarized with an applied electric field. A method of controlling a thick-film transducer array comprises exciting one or more array elements to generate a wavefield in a substrate volume, the wavefield can be reflected by features within the substrate volume, one or more array elements can receive reflected wavefield signals, and images of the insonified substrate volume can be generated.

Determination of presence or absence of a defect having irregular position, size, shape, and/or the like in an image are made automatically. An inspection device includes: an inspection image obtaining section that obtains an inspection image used to determine presence or absence of an internal defect in an inspection target; and a defect presence/absence determining section that determines presence or absence of a defect with use of a restored image generated by inputting the inspection image into a generative model constructed by machine learning that uses, as training data, an image of an inspection target in which a defect is absent, the generative model being constructed so as to generate a new image having a similar feature to that of an image input into the generative model.