The present disclosure provides methods and systems for the characterization of a microtexture of a sample, component, or the like. The methods may include methods of determining a service life limiting region of a component, determining a treatment method for a component, and/or selecting components from a batch of components for use in production. The characterization may include calculating a microtexture level indicator from ultrasonic C-scan images for various samples, regions, components, or the like. The microtexture level indicator may include at least one of an average peak factor, a standard deviation of peak amplitude, and/or a baseband bandwidth.

Disclosed herein are systems and methods for mechanical failure monitoring, detection, and classification in electronic assemblies. In some embodiments, a mechanical monitoring apparatus may include: a fixture to receive an electronic assembly; an acoustic sensor; and a computing device communicatively coupled to the acoustic sensor, wherein the acoustic sensor is to detect an acoustic emission waveform generated by a mechanical failure of the electronic assembly during testing.

The present disclosure are directed to techniques for defect detection and identification inside batteries. In one aspect, a non-invasive method of identifying and labeling defects in a battery cell includes transmitting acoustic signals through a battery cell via one or more first transducers, receiving response signals in response to the acoustic signals at one or more second transducers, determining whether at least one feature of interest exists in the battery cell based on analyzing the response signals, performing an identification and labeling process on the at least one feature of interest to determine at least one defect in the battery cell, and outputting a result of the identification and labeling process.

According to an embodiment, a diagnostic apparatus includes a sound-emitting unit, at least one measurement unit, and a processor. The sound-emitting unit includes a plurality of speakers arranged at equal angular intervals on a circumference of a circle, and is configured to emit a first vibration sound to a target by using the speakers. The at least one measurement unit is arranged on a central axis of the circle, and is configured to measure a vibration of the target generated in response to the first vibration sound, or a second vibration sound radiated from the target due to the vibration. The processor is configured to diagnose the target based on an output from the at least one measurement unit.

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 an air pocket, delamination, or foreign object within the composite. Furthermore, in one embodiment, the system includes an artificial intelligence capable of highlighting foreign objects within the 3-D image in real time or near real time and providing data regarding each object area, such as the depth, size, and/or type of each defect.

Embodiments of the invention include a system and method for detecting wood-boring species of insects in a structure (16), involving one or more primary (14) and reference (18) sensors and a signal conditioning and acquisition device (22) capable of being coupled to the sensors (14, 18). The system (10) also includes a processor (24) capable of being coupled to a non-transitory, computer-readable storage medium that includes program logic for execution by the processor (24). The program logic includes a logic module that receives signals originating from the sensors (14, 18) and discriminates between noise generated by any wood boring species in the structure and extraneous noise unrelated to the wood boring species of insects. The extraction of signal features based on pulse duration, signal spectra and signal envelope spectra can be used for insect pulse discrimination. A sound-suppressing sensor assembly (12) can be weighted to enhance the coupling of the primary sensor (14) with the structure (16) being tested.

A device for imaging defects in a structure includes N transmitters and P receivers to be distributed over at least one surface of the structure and a central unit controlling the transmitters and receivers to sequentially record Q≤N×P signals (S) obtained from electrical signals provided by the receivers of Q different transmitter/receiver pairs, after reception of mechanical waves transmitted by the transmitters of these Q pairs. It further stores Q first and Q second corresponding reference signals (S.sub.REF1, S.sub.REF2), representative of the structure without defects and differing by random noise. A central processing unit is programmed to: correlate each signal obtained with the corresponding first reference signal, in such a way as to construct an image of probabilities of defects; correlate each first reference signal with the corresponding second reference signal, in such a way as to construct a reference noisy image; and subtract the reference noisy image from the image of probabilities of defects.

A technological solution for analyzing a sequence of ultrasound scan images of an asset and diagnosing a health condition of a section of the asset. The solution includes receiving, by a machine learning platform, an ultrasound scan image of the section of the asset; analyzing, by the machine learning platform, the ultrasound scan image to detect any aberrations in the section; generating, by the machine learning platform, an aberration label for each detected aberration in the section; labeling, by the machine learning platform, the section of the asset with a section condition label; and, rendering, by a display device, the section conditional label. The section condition label can be based on each detected aberration in the section. The section condition label can include at least one of an aberration area ratio, a total number of aberrations, and the aberration label for each detected aberration in the section of the asset.

A reflection-diffraction-deformation flaw detection method employs a transverse wave oblique probe. When an ultrasonic transverse wave encounters a defect during propagation, a reflected wave, a diffracted wave, and a deformed wave are generated. Through a comprehensive analysis of these waves, the presence or absence of the defect is determined by the reflected wave having reflection characteristics and the diffracted wave having the diffraction characteristics. The shape and size of the defect are determined by the deformed wave having deformation characteristics, namely the deformed surface wave generated at the endpoints of the defect which propagates on the defect surface. Furthermore, by the combination of paths trailed by the deformed surface wave, the deformed transverse wave, and the deformed longitudinal wave that are generated by the defect as well as that trailed by the transmit transverse wave, causes of all those waves in the screen can be revealed.

Techniques are provided for detecting wafer defects. Example techniques include exciting a wafer using an acoustic signal to cause the wafer to exhibit vibrations, measuring one or more of linear frequency response metrics or nonlinear frequency responses metrics associated with the vibrations, and identifying any defects in the wafer based at least in part on one or more of the linear frequency response metrics or nonlinear frequency responses metrics. In embodiments, the wafer includes bismuth telluride (Bi.sub.2Te.sub.3).