Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Approximation theory and feedforward networks
Neural Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Design and Analysis of Experiments
Design and Analysis of Experiments
Neural network-based adaptive controller design of robotic manipulators with an observer
IEEE Transactions on Neural Networks
Neural network classification: a Bayesian interpretation
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Performance evaluation of multilayer perceptrons in signal detection and classification
IEEE Transactions on Neural Networks
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This 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.