Analysis of the welding process sound using Convolutional Neural Networks for penetration state recognition
Authors
Term
4. term
Education
Publication year
2020
Submitted on
2020-06-19
Pages
28
Abstract
With a reliable and accurate method for real-time feedback being a necessity for robotic welding, various methods have over time been proposed for online weld monitoring. This paper investigates the use of Convolutional Neural Networks (CNNs) to analyze the sound signature from the welding arc, which contains information about the penetration state of the weld pool. The sound is transformed into a spectrogram image using a series of STFT transforms and the image is input into a CNN which classifies it as one of three possible penetration states. Given a pre-recorded dataset, the authors investigate how different sampling strategies impact the ability of the network to generalize to unseen examples. Further it is investigated, how various pre-processing parameters used when creating the spectrogram impact the prediction accuracy of the network. Using the optimal sampling strategy and pre processing parameters a custom CNN is built and tuned, achieving an average testing accuracy of 69,94% in recognizing the penetration state based on a sound sample of 0,25s.
Keywords
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