New Approach in Predicting Aluminum-Alloy Properties Using Neural Networks
In this study the impact of alloying elements on hardness, for different series of Aluminum alloys, was analyzed and studied using a mathematical modeling tool based on Artificial Neural Networks (ANN). This new model was developed as multi-layer feed forward network with a hidden layer that can approximate any complex function and back-propagation learning algorithm. Thus, it is possible by using this model to predict properties of Aluminum alloys, in particular hardness, based on certain punch of inputs. The latter includes the weight percentage of the compositioncontents of various metals such as Cr, Cu, Fe, Mg, Mn, Si, Ti, Zn, and the Aluminum.In the current research work, several runs were made using this newly developed model, and published results of previous experimental research, available in open literature.The obtained results here were found in full agreement with those reported by other researchers. Thus, it is deemed that such model could help researchers and industries in designing the desired Aluminum alloy and predicting its hardness at no cost. Equally important is relatively high accuracy of obtained results.
Keywords - Aluminum alloy, Alloying elements, hardness, Neural Networks.