A neural network model was developed to predict the weld pool shape for pulsed-laser aluminum welds. Several different network architectures were examined and the optimum architecture was identified. The neural network was then trained and, in spite of the small size of the training data set, the network accurately predicted the weld pool shape profiles. The neural network output was in the form of four weld pool shape parameters (depth, width, half-width, and area) and these were converted into predicted weld pool profiles with the use of the actual experimental poo1 profiles as templates. It was also shown that the neural network model could reliably predict the change from conduction-mode type shapes to keyhole-mode shapes
