Predictions of temperature distributions on layered metal plates using artificial neural networks

The back propagation learning algorithm with three different variants, single layer and logistic sigmoid transfer function have been used in the network. By using the weights of the network, formulations have been given for each output. The network has yielded R2 values of 0.999 and the mean percent errors are smaller than 0.8 for the training data, while the R2 values are about 0.999 and the mean percent errors are smaller than 0.7 for the test data. The analysis has been extended for different materials and for the different temperature values that have been applied. The Al/CrNi laminated plate has a lower temperature gradient distribution on the upper (or non-heated) surface due to its lesser heat conductivity compared to the Cu/CrNi steel. The thickness of 8 mm provides the best results among the alloys that have been considered.