Abstract
In this article, an in-process monitoring scheme for a pulsed Nd:YAG laser spot welding (LSW) is presented. Acoustic emission (AE) was selected for the feedback signal, and the AE data during LSW were sampled and analyzed for varying process conditions such as laser power and pulse duration. In the analysis, possible AE generation sources such as melting and solidification mechanism during welding were investigated using both the time- and frequency-domain signal processings. The results, which show close relationships between LSW and AE signals, were adopted in the feature (input) selection of a back-propagation artificial neural network, to predict the weldability of stainless steel sheets. Processed outputs agree well with LSW experimental data, which confirms the usefulness of the proposed scheme.
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