ACOUSTIC SENSING FOR METAL TRANSFER MODE AND PENETRATION STATE CLASSIFICATION OF GMAW USING ARTIFICIAL NEURAL NETWORKS

Bidstrup, Anders

4. term

Manufacturing Technology, Master

2017

2017-06-02

79 pages

This report covers a study of the acoustic response of GMAW's capabilities within machine learning-based weld quality monitoring. Initially it is determined to train an artificial neural network, ANN, to classify two metal transfer modes - globular transfer and shortcircuit transfer - and three penetration states - lack of penetration, full penetration and excessive penetration - based on related work. To do so, 1166 features are extracted for each window of acoustic signal consisting of a range of temporal-, spectral shape-, harmonic- and perceptual features as well as statistical features from a wavelet packet decomposition. Classification data is produced in a robotic GMAW cell by provoking the desired classi- cation states. The acquired data is then preprocessed and input to a function made to train 110 ANN congurations for 15 combinations of window size and overlap using both gradient descent with adaptive learning rate, GDA, and scaled conjugate gradient, SCG, descent. Based on the trained ANNs it was concluded that classification of the three penetration states was possible for ANNs trained using SCG and partially possible if they are trained using GDA. Furthermore, the results for whether classification of metal transfer mode is possible were inconclusive but showed a tendency of correct prediction.

Maskinlæring ; Neuralt netværk ; monitorering ; feature extraction ; GMAW ; MIG ; MAG

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