Abstract
With the increasing diversity and complexity of products, the determination of welding sequences becomes more and more difficult under widely varying conditions. In this study, we applied the Hopfield neural net model to this problem with the aim of mitigating these difficulties. Numerical experiments were carried out on a PC with respect to two models:(1) productivity first and (2) quality first. The results agreed quite well with those of past experience and it is verified that the neural net approach is quite effective for such a multiple objective problem.
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