Non-limiting examples of the present disclosure relate to devices, systems and methods of manufacture for an exemplary waveguide usable for acoustic signal transmission for non-destructive evaluation (NDE) of a specimen (e.g., a wooden specimen) as well as apparatuses usable therewith. An exemplary waveguide comprises a mating portion for interfacing with a transducer horn of an ultrasonic transducer. The mating portion comprises at least a contact well configured to enable a connection between the transducer horn and the waveguide. The waveguide further comprises a body portion that comprises an upper body portion, that has a flat-faced distal end that is usable to establish contact with a surface of the specimen, and a lower body portion that is attached to and extends outwardly from the upper body portion and is further attached to the mating portion. Other technical examples are further described in the present disclosure.

Non-limiting examples of the present disclosure relate to devices, systems and methods of manufacture for an exemplary waveguide usable for acoustic signal transmission for non-destructive evaluation (NDE) of a specimen (e.g., a wooden specimen) as well as apparatuses usable therewith. An exemplary waveguide comprises a mating portion for interfacing with a transducer horn of an ultrasonic transducer. The mating portion comprises at least a contact well configured to enable a connection between the transducer horn and the waveguide. The waveguide further comprises a body portion that comprises an upper body portion, that has a flat-faced distal end that is usable to establish contact with a surface of the specimen, and a lower body portion that is attached to and extends outwardly from the upper body portion and is further attached to the mating portion. Other technical examples are further described in the present disclosure.

An inspection apparatus comprises a chassis position/attitude estimator to estimate position/attitude information of a moving body and generate a chassis position/attitude estimation signal, a hammering tester hammer part error signal generator to generate a hammering tester hammer part error signal, a hammering tester hammer part position/attitude signal generator to generate a hammering tester hammer part position/attitude signal, a first sensor data frequency characteristic interpolator to generate a first sensor data frequency characteristic interpolation signal from the received chassis position/attitude estimation signal, a second sensor data frequency characteristic interpolator to generate a second sensor data frequency characteristic interpolation signal from the received hammering tester hammer part error signal and the received hammering tester hammer part position/attitude signal, and a hammering tester hammer part position/attitude estimator to generate a hammering tester hammer part position/attitude estimation signal from the received first sensor data frequency characteristic interpolation signal and the received second sensor data frequency characteristic interpolation signal.

An inspection apparatus comprises a chassis position/attitude estimator to estimate position/attitude information of a moving body and generate a chassis position/attitude estimation signal, a hammering tester hammer part error signal generator to generate a hammering tester hammer part error signal, a hammering tester hammer part position/attitude signal generator to generate a hammering tester hammer part position/attitude signal, a first sensor data frequency characteristic interpolator to generate a first sensor data frequency characteristic interpolation signal from the received chassis position/attitude estimation signal, a second sensor data frequency characteristic interpolator to generate a second sensor data frequency characteristic interpolation signal from the received hammering tester hammer part error signal and the received hammering tester hammer part position/attitude signal, and a hammering tester hammer part position/attitude estimator to generate a hammering tester hammer part position/attitude estimation signal from the received first sensor data frequency characteristic interpolation signal and the received second sensor data frequency characteristic interpolation signal.

An acoustic garbage classification method using a one-dimensional convolutional neural network (1D-CNN) is provided. The method includes: acquiring sound signals generated by falling garbage; preprocessing the sound signals; acquiring and preprocessing the sound signals of different types of garbage, building a sound database for garbage classification, and establishing and training a 1D-CNN model; acquiring a sound signal of garbage to be classified, and inputting the sound signal into the trained 1D-CNN for garbage classification to obtain a classification result. The present disclosure is helpful to assist people in accurate garbage classification, improves the accuracy of garbage classification and recycling, and has high practical and popularization value.

A scanning apparatus for imaging an object, comprising an ultrasound transducer comprising a transmitter configured to transmit ultrasound signals in a first direction towards an object and a receiver configured to receive reflected ultrasound signals from an object; and a backing block for absorbing ultrasound signals, located adjacent the transducer along a second direction opposite to the first direction; the backing block comprising an inner surface facing the transducer, the inner surface comprising a non-planar feature configured to increase the absorption of ultrasound signals by the backing block.

A scanning apparatus for imaging an object, comprising an ultrasound transducer comprising a transmitter configured to transmit ultrasound signals in a first direction towards an object and a receiver configured to receive reflected ultrasound signals from an object; and a backing block for absorbing ultrasound signals, located adjacent the transducer along a second direction opposite to the first direction; the backing block comprising an inner surface facing the transducer, the inner surface comprising a non-planar feature configured to increase the absorption of ultrasound signals by the backing block.

A method for classifying a glass object via acoustic analysis by a classifying apparatus is provided. The method including: receiving, by a processor, sound data of a knock sound generated by applying a knocking operation on the glass object; determining, by the processor, a type of the glass object by performing a knock-sound analysis to the sound data, wherein the type of the glass object includes an organic glass and an inorganic glass; if the type of the glass object is determined as the inorganic glass, receiving, by the processor, echo data of an echo induced by applying an ultrasonic-echo operation on the glass object; and determining, by the processor, a further type of the glass object by performing an echo-decay analysis to the echo data, wherein the further type of the glass object includes a crystal glass, a borosilicate glass and a soda-lime glass.

A method for classifying a glass object via acoustic analysis by a classifying apparatus is provided. The method including: receiving, by a processor, sound data of a knock sound generated by applying a knocking operation on the glass object; determining, by the processor, a type of the glass object by performing a knock-sound analysis to the sound data, wherein the type of the glass object includes an organic glass and an inorganic glass; if the type of the glass object is determined as the inorganic glass, receiving, by the processor, echo data of an echo induced by applying an ultrasonic-echo operation on the glass object; and determining, by the processor, a further type of the glass object by performing an echo-decay analysis to the echo data, wherein the further type of the glass object includes a crystal glass, a borosilicate glass and a soda-lime glass.

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.