Methods and systems for classifying the type and severity of defects in welds
First Claim
1. A method for determining a type of a defect in a weld comprising:
- determining a defect location and a defect signal corresponding to the defect location by analyzing ultrasonic response signals collected from a plurality of measurement locations along the weld;
inputting the defect signal and a plurality of defect proximity signals into a trained artificial neural network, wherein the plurality of defect proximity signals correspond to ultrasonic response signals from measurement locations on each side of the defect location and the trained artificial neural network is 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.
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Accused Products
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.
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Citations
20 Claims
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1. A method for determining a type of a defect in a weld comprising:
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determining a defect location and a defect signal corresponding to the defect location by analyzing ultrasonic response signals collected from a plurality of measurement locations along the weld; inputting the defect signal and a plurality of defect proximity signals into a trained artificial neural network, wherein the plurality of defect proximity signals correspond to ultrasonic response signals from measurement locations on each side of the defect location and the trained artificial neural network is 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. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A method for determining a severity of a defect in a weld comprising:
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determining a defect location and a defect signal corresponding to the defect location by analyzing ultrasonic response signals from a plurality of measurement locations along the weld; inputting the defect signal and a plurality of defect proximity signals into a trained artificial neural network, wherein the plurality of defect proximity signals correspond to ultrasonic response signals from measurement locations on each side of the defect location and the trained artificial neural network is operable to; determine a severity classification of the defect located at the defect location based on the defect signal and the plurality of defect proximity signals; and output the severity classification of the defect located at the defect location. - View Dependent Claims (10, 11, 12, 13)
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14. A defect classification system for identifying a type of a defect in a weld, the defect classification system comprising a controller, an acoustic signal generator, an acoustic signal detector, and a positioning device, wherein the acoustic signal generator, the acoustic signal detector and the positioning device are electrically coupled to the controller and the controller is programmed to:
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induce ultrasonic signals at multiple measurement locations along the weld with the acoustic signal generator; collect an ultrasonic response signal from each of the measurement locations with the acoustic signal detector and store each ultrasonic response signal in a memory operatively associated with the controller; determine a defect location and a defect signal by analyzing the ultrasonic response signal from each of the measurement locations; determine a plurality of defect proximity signals, wherein the plurality of defect proximity signals correspond to ultrasonic response signals from measurement locations on each side of the defect location; input the defect signal and the plurality of defect proximity signals into a trained artificial neural network operatively associated with the controller, wherein the trained artificial neural network is 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. - View Dependent Claims (15, 16, 17, 18, 19, 20)
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Specification