An improved neural network model for prediction of mechanical properties of magnesium alloys

An improved neural network model was developed for prediction of mechanical properties in the de-sign and development of new types of magnesium alloys by refining the types of input variables and using a more reasonable algorithm. The results showed that the improved model apparently decreased the prediction errors, and raised the accuracy of the prediction results. Better preprocessing parame-ters were found to be [0.15, 0.90] for the tensile strength, [0.1, 0.9] for the yield strength, and [0.15, 0.90] for the elongation. When the above parameters were used, the relativity for predicition of strength was bigger than 0.95. By using improved ANN analysis, more reasonable process parameters and compo- sition could be obtained in some magnesium alloys without addition of strontoum.