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dc.contributor.authorBoldsaikhan, Enkhsaikhan
dc.contributor.authorCorwin, Edward M.
dc.contributor.authorLogar, Antonette M.
dc.contributor.authorArbegast, William J.
dc.identifier.citationEnkhsaikhan Boldsaikhan, Edward M. Corwin, Antonette M. Logar, William J. Arbegast The use of neural network and discrete Fourier transform for real-time evaluation of friction stir welding Applied Soft Computing, v.11, no. 8, December 2011, pp. 4839–4846en_US
dc.descriptionClick on the DOI link to access the article (may not be free)en_US
dc.description.abstractThis paper introduces a novel real-time approach to detecting wormhole defects in friction stir welding in a nondestructive manner. The approach is to evaluate feedback forces provided by the welding process using the discrete Fourier transform and a multilayer neural network. It is asserted here that the oscillations of the feedback forces are related to the dynamics of the plasticized material flow, so that the frequency spectra of the feedback forces can be used for detecting wormhole defects. A one-hidden-layer neural network trained with the backpropagation algorithm is used for classifying the frequency patterns of the feedback forces. The neural network is trained and optimized with a data set of forge-load control welds, and the generality is tested with novel data set of position control welds. Overall, about 95% classification accuracy is achieved with no bad welds classified as good. Accordingly, the present paper demonstrates an approach for providing important feedback information about weld quality in real-time to a control system for friction stir welding.en_US
dc.relation.ispartofseriesApplied Soft Computing;v.11, issue 8
dc.subjectNeural networksen_US
dc.subjectDiscrete Fourier transformen_US
dc.subjectFriction stir weldingen_US
dc.subjectFrequency pattern recognitionen_US
dc.titleThe use of neural network and discrete Fourier transform for real-time evaluation of friction stir weldingen_US

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