Provided is a test system for hard rock breaking by a microwave intelligent loading based on true triaxial stress, including: a true triaxial stress loading device consisting of a loading frame and a rock sample moving structure; a microwave-induced hard rock breaking device consisting of an excitation cavity, a rectangular waveguide, a magnetron, a thermocouple, a circulator, a cold water circulation device, a flowmeter, a power meter, an automatic impedance tuner, a coupler, a microwave heater and a shielding cavity; and a dynamic rock response monitoring and intelligent microwave parameter control system consisting of a CCD industrial camera, a temperature acquisition device and an anti-electromagnetic high-temperature resistant acoustic wave-acoustic emission integrated sensor. According to the test system, the microwave-induced hard rock breaking test, dynamic monitoring temperature and rock breaking in microwave-induced breaking process and intelligent control over microwave power and heating time are achieved.

A performance evaluation method for elastic material including rubber or elastomer, the method includes a step of applying a strain to a test piece made of the elastic material to form at least one void inside the test piece, a step of obtaining projected images of the test piece by irradiating the test piece with X-rays at a plurality of times after the at least one void is formed, and a step of obtaining a volume change of the at least one void between the plurality of times based on the projected images, as one of indexes of performance.

A performance evaluation method for elastic material including rubber or elastomer, the method includes a step of applying a strain to a test piece made of the elastic material to form at least one void inside the test piece, a step of obtaining projected images of the test piece by irradiating the test piece with X-rays at a plurality of times after the at least one void is formed, and a step of obtaining a volume change of the at least one void between the plurality of times based on the projected images, as one of indexes of performance.

This application relates to an apparatus and method for detecting a microcrack using orthogonality analysis of a mode shape vector and a principal plane in a resonance point. The apparatus may include a measurement unit comprising multiple sensors and configured to measure whether a crack exists at a measurement target, and an analysis unit configured to determine whether a crack exists, on the basis of measurement values of the respective sensors. The measurement unit includes a fixing jig configured to fix the measurement target, an excitation means configured to apply a predetermined impact to the measurement target, and multiple acceleration sensors attached at predetermined locations on the measurement target. The analysis unit may further calculate frequency responses of the measurement target to the impact applied by the excitation means, and determine whether a crack exists by analyzing the number of resonance points and independence of the resonance points.

The present invention relates to a method for evaluating physical properties of a thin film specimen and a thin film specimen for a tensile test of the present invention, and according to the present invention, reliability of measured physical properties can be increased, and an abnormal damage of a thin film specimen can be suppressed by analyzing the strain rate of a speckle pattern formed on the thin film specimen by using a digital image correlation analysis scheme during a tensile test of the thin film specimen.

The present invention relates to a method for evaluating physical properties of a thin film specimen and a thin film specimen for a tensile test of the present invention, and according to the present invention, reliability of measured physical properties can be increased, and an abnormal damage of a thin film specimen can be suppressed by analyzing the strain rate of a speckle pattern formed on the thin film specimen by using a digital image correlation analysis scheme during a tensile test of the thin film specimen.

An auxiliary prediction system is provided to predict reliability of an object after a specific operation is applied to the target object. The auxiliary prediction system includes an image correction module and an analysis module. The image correction module performs an image correction procedure to convert an original image of the target object into a first correction image. The analysis module performs a feature analysis on the first correction image through an artificial intelligence model that has been trained, so as to predict whether the target object has a defect or not after the specific operation.

Techniques are described for a materials test controller that includes a Field-Programmable Gate Array (FPGA). The FPGA is configured for acquiring sensor data from sensor device(s) that measure the current state of sample material(s) on which the materials testing is performed. For controlling the actuator device, the FPGA generates a waveform of setpoints; each setpoint represents a desired state of the materials testing. Based on the sensor data, the FPGA calculates process variable(s) for input to a control logic of the FPGA. Using multiple iterations to generate output signals for the actuator device, the control logic receives the process variable(s) and a setpoint of, and based at least on these received inputs, generates an output signal. The output signal of the FPGA causes the actuator device to transition from the current state to a new state that is closer to the desired state as measured by the sensor devices.

Techniques are described for a materials test controller that includes a Field-Programmable Gate Array (FPGA). The FPGA is configured for acquiring sensor data from sensor device(s) that measure the current state of sample material(s) on which the materials testing is performed. For controlling the actuator device, the FPGA generates a waveform of setpoints; each setpoint represents a desired state of the materials testing. Based on the sensor data, the FPGA calculates process variable(s) for input to a control logic of the FPGA. Using multiple iterations to generate output signals for the actuator device, the control logic receives the process variable(s) and a setpoint of, and based at least on these received inputs, generates an output signal. The output signal of the FPGA causes the actuator device to transition from the current state to a new state that is closer to the desired state as measured by the sensor devices.

Analysis of residual stress in materials is often done in static conditions in a laboratory. Accurate systems and methods for performing these analyses in a dynamic, non-laboratory environment are notoriously difficult and can be very inaccurate. A method using a portable, field deployable apparatus having greater accuracy than currently available is disclosed whereby accurate and repeatable residual stress analysis may be implemented in non-laboratory environments leading to greatly improved diagnostics, maintenance and life limit prediction.