Systems and methods for bondline inspection using mechanical wave measurement and gas excitation. A cost-effective optical interferometry technique is used to measure mechanical waves generated by gas excitation, which measurements may be used to verify the strength of a bondline of a composite bonded structure. A gas gun which produces a high-pressure short-pulsewidth gas pulse at the front free surface of the composite material. A velocity interferometer system for any reflector (VISAR) is synchronized with the controlled gas pulsation and used to measure the surface velocities. The respective shock wave-induced displacements of the back and front free surfaces are then calculated. The measured free surface displacements are compared with calibrated thresholds to determine whether a weak bond has been detected or not. Optionally, a ring magnet is aligned exactly under the gas gun nozzle to enable VISAR beam centering.

Structural health monitoring (SHM)/nondestructive evaluation (NDE) exists as a tool in conjunction with manufactured pieces. Presently disclosed subject matter integrates automated video with a structural health monitoring system. In conjunction with bridge monitoring, integration of such two systems automates determination of the effect or correlation of vehicular loading on SHM data from a subject bridge. Such correlations help to understand the sources of structural health monitoring data, particularly acoustic emission data, in bridges and other structures, such as dams and nuclear plants. Automation of the evaluation of bridges and other structures increases accuracy and minimizes risk to workers and the public. Assessing the structural condition of bridges and other structures as presently disclosed also facilitates automated asset management of transportation systems, such as by state departments of transportation and other bridge/structural owners.

A non-destructive inspection system is presented. The non-destructive inspection system comprises a robotic end effector having an extendable actuator and a flange-mounted roller containing an ultrasonic sensor, the flange-mounted roller connected to the extendable actuator by a pivot connection, the extendable actuator configured to extend the flange-mounted roller until the flange-mounted roller contacts an inspection surface.

An acoustic inspection system can be used to generate a surface profile of a component under inspection, and then can be used to perform the inspection on the component. The acoustic inspection system can obtain acoustic imaging data, e.g., FMC data, of the component. Then, the acoustic inspection system can apply a previously trained machine learning model to an encoded acoustic image, such as a TFM image, to generate a representation of the profile of one or more surfaces of the component. In this manner, no additional equipment is needed, which is more convenient and efficient than implementations that utilize additional components that are external to the acoustic inspection system.

The disclosure discloses a stress gradient high-efficiency non-destructive detection system based on frequency domain calculation of broadband swept frequency signals, and a detection method thereof. The detection method includes: step 1: calibrating an LCR wave velocity of an object to be measured; step 2: calculating a starting frequency and a cut-off frequency of broadband swept frequency signals based on the LCR wave velocity of the object to be measured in the step 1 and a stress gradient measuring range in a depth direction of the object to be measured; step 3: converting phase delay to time delay information based on the phase delay of the starting frequency and the cut-off frequency in the step 2; and step 4: determining stresses of depths corresponding to different frequency components based on the time delay information in the step 3 to finally realize layer-by-layer scanning of stresses at different depths of the measured object. The disclosure is used to solve the problem of low stress gradient measuring accuracy, and realize the high-efficiency characterization of the stress gradient in the depth direction.

This method comprises: generating, only using the device, a low-frequency signal that makes the structure vibrate, generating a high-frequency signal in the structure, measuring a vibratory signal caused by the generated low-frequency and high-frequency signals at the same time then adaptively re-sampling these measurements to obtain a re-sampled vibratory signal the power spectrum of which comprises: a first frequency range [u.sub.BFmin; u.sub.BFmax] of width larger than 5 Hz that contains 95% of the power of the low-frequency signal, a second frequency range [u.sub.HFmin; u.sub.HFmax] of width systematically smaller than u.sub.BFmin that contains 95% of the power of the low-frequency signal, signaling a defect in the structure if an additional power lobe is detected outside of the ranges [u.sub.BFmin; u.sub.BFmax] and [u.sub.HFmin; u.sub.HFmax].

This application relates to an apparatus and method for analyzing the damping characteristics of a carbon composite material. Damping analysis of carbon composite material using modal damping ratio is a conventional method that cannot accurately represent viscous damping coefficient variation but has errors in a sensitivity analysis. The damping characteristics of the carbon composite material were described by a parallel combination of the viscous damping coefficient of carbon fiber and binding matrix. The damping characteristics of the carbon composite material were expressed with the sensitivity index calculated only from the viscous damping coefficient of the carbon fiber by removing the viscous damping coefficient of the binding matrix that does not change depending on the carbon fiber direction. Various embodiments improve accuracy in analyzing the damping characteristics of carbon composite material.

This application relates to a method of analyzing dynamic characteristics of a carbon composite material. This application also relates to a method of calculating sensitivity indices for structural stiffness and a viscous damping coefficient of a carbon composite material and a method of analyzing dynamic characteristics of a carbon composite material by using the same. Respective sensitivity indices for structural stiffness and a viscous damping coefficient according to a direction (angle) of carbon fiber for a carbon composite material are calculated. A change in the dynamic characteristics of the carbon composite material is evaluated through a proportional relationship between the sensitivity indices, thereby conducting a more accurate and efficient analysis.

Disclosed are devices, systems and methods for touch, force and/or thermal sensing by an ultrasonic transceiver chip. In some aspects, an ultrasonic transceiver sensor device includes a semiconductor substrate; a CMOS layer attached to the substrate; an array of piezoelectric transducers coupled to the CMOS layer to generate ultrasonic pulses; and a contact layer attached to the substrate on a side opposite the substrate for providing a surface for contact with an object, where an ultrasonic pulse generated by a piezoelectric transducer propagates through the substrate and the contact layer, such that when the object is in contact with the surface of the contact layer, a reflected ultrasonic pulse is produced and propagates through the contact layer and the substrate to be received at the array of piezoelectric transducers, and the CMOS layer receive and process outputs from the piezoelectric transducers produced in response to the received reflected ultrasonic pulses.

Systems and methods for tracking the location of a non-destructive inspection (NDI) scanner using images of a target object acquired by the NDI scanner. The system includes a frame, an NDI scanner supported by the frame, a system configured to enable motorized movement of the frame, and a computer system communicatively coupled to receive sensor data from the NDI scanner and track the location of the NDI scanner. The NDI scanner includes a two-dimensional (2-D) array of sensors. Subsurface depth sensor data is repeatedly (recurrently, continually) acquired by and output from the 2-D sensor array while at different locations on a surface of the target object. The resulting 2-D scan image sequence is fed into an image processing and feature point comparison module that is configured to track the location of the scanner relative to the target object using virtual features visible in the acquired scan images.