Abstract: In-process weld geometry methods and systems are discussed, enabled, and provided. Some embodiments include in-process welding devices to compensate for error associated with detected weld penetration depth. Exemplary devices can generally include an ultrasonic energy source, an ultrasonic receiving sensor, and a controller. The ultrasonic energy source can be disposed to generate ultrasonic energy through a first specimen being welded to a second specimen. A weld seam can be used to join the first specimen to the second specimen. The ultrasonic sensor can be disposed on an opposite side of the weld seam from the ultrasonic energy source, and configured to detect ultrasonic energy propagated from the first specimen side of the weld seam to the second specimen side of the weld seam.

Abstract: A method for processing ultrasonic response signals collected from a plurality of measurement locations along a weld of a test sample to determine the presence of defects in the weld may include filtering an ultrasonic response signal from each measurement location to produce a plurality of filtered response signals for each measurement location, wherein each filtered response signal corresponds to specific types of defects. Thereafter, a plurality of energy distributions may be calculated for the weld based on the plurality of filtered response signals for each measurement location. The plurality of energy distributions may be compared to corresponding baseline energy distributions to determine the presence of defects in the weld.

Abstract: A method for processing ultrasonic response signals collected from a plurality of measurement locations along a weld to determine the presence of a defect in the weld may include filtering an ultrasonic response signal from each of the measurement locations to produce a filtered response signal for each of the measurement locations. Thereafter, an ultrasonic energy for each of the measurement locations is calculated with the corresponding filtered response signal. The ultrasonic energy for each measurement location is then compared to the ultrasonic energy of adjacent measurement locations to identify potential defect locations. When the ultrasonic energy of a measurement location is less than the ultrasonic energy of the adjacent measurement locations, the measurement location is a potential defect location. The presence of a defect in the weld is then determined by analyzing fluctuations in the ultrasonic energy at measurement locations neighboring the potential defect locations.

Abstract: A method for determining the type of a defect in a weld may include determining a defect location and a corresponding defect signal by analyzing ultrasonic response signals collected from a plurality of measurement locations along the weld. The defect signal and the plurality of defect proximity signals corresponding to ultrasonic response signals from measurement locations on each side of the defect location may then be input into a trained artificial neural network. The trained artificial neural network may be operable to identify the type of the defect located at the defect location based on the defect signal and the plurality of defect proximity signals and output the type of the defect located at the defect location. The trained artificial neural network may also be operable to determine a defect severity classification based on the defect signal and the plurality of defect proximity signals and output the severity classification.

Abstract: A method for processing ultrasonic response signals collected from a plurality of measurement locations along a weld to determine the presence of a defect in the weld may include filtering an ultrasonic response signal from each of the measurement locations to produce a filtered response signal for each of the measurement locations. Thereafter, an ultrasonic energy for each of the measurement locations is calculated with the corresponding filtered response signal. The ultrasonic energy for each measurement location is then compared to the ultrasonic energy of adjacent measurement locations to identify potential defect locations. When the ultrasonic energy of a measurement location is less than the ultrasonic energy of the adjacent measurement locations, the measurement location is a potential defect location. The presence of a defect in the weld is then determined by analyzing fluctuations in the ultrasonic energy at measurement locations neighboring the potential defect locations.

Abstract: A method for determining the type of a defect in a weld may include determining a defect location and a corresponding defect signal by analyzing ultrasonic response signals collected from a plurality of measurement locations along the weld. The defect signal and the plurality of defect proximity signals corresponding to ultrasonic response signals from measurement locations on each side of the defect location may then be input into a trained artificial neural network. The trained artificial neural network may be operable to identify the type of the defect located at the defect location based on the defect signal and the plurality of defect proximity signals and output the type of the defect located at the defect location. The trained artificial neural network may also be operable to determine a defect severity classification based on the defect signal and the plurality of defect proximity signals and output the severity classification.

Abstract: A method for processing ultrasonic response signals collected from a plurality of measurement locations along a weld of a test sample to determine the presence of defects in the weld may include filtering an ultrasonic response signal from each measurement location to produce a plurality of filtered response signals for each measurement location, wherein each filtered response signal corresponds to specific types of defects. Thereafter, a plurality of energy distributions may be calculated for the weld based on the plurality of filtered response signals for each measurement location. The plurality of energy distributions may be compared to corresponding baseline energy distributions to determine the presence of defects in the weld.

Abstract: A switching device comprises at least two base racks, each base rack including a switch card in communication with a line card across a backplane, the line card having at least an external port. The at least two base racks are coupled such that the switch cards of each are linked. A method for switching a packet comprises introducing the packet into an external port on a first base rack, transmitting the packet from a first cascade port on the first base rack to a second cascade port on a second base rack, and sending the packet out of the second base rack through a second external port.