Artificial neural networks for prediction of quality in resistance spot welding
DOI:
https://doi.org/10.3989/revmetalm.2006.v42.i5.32Keywords:
Resistance spot welding, Metallurgical quality, Artificial neural networksAbstract
An artificial neural network is proposed as a tool for predicting from three parameters (weld time, current intensity and electrode sort) if the quality of a resistance spot weld reaches a certain level or not. The quality is determined by cross tension testing. The fact of reaching this quality level or not is the desired output that goes with each input of the artificial neural network during its supervised learning. The available data set is made up of input/desired output pairs and is split randomly into a training subset (to update synaptic weight values) and a validation subset (to avoid overfitting phenomenon by means of cross validation).
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