Using Artificial Neural Networks to Predict Hardness and Impact Toughness of Aluminum Alloy 6061-T6

Abstract

Predicting the material's mechanical properties is essential for reducing testing time, cost, and effort. In this study, the effect of temperature and holding time on the hardness and impact toughness of Al 6061 was investigated using the design of experiments (DOE) methodology. Analysis of variance (ANOVA) was used to analyze the results of DOE-factorial experiments. Two factors with five replicates were studied in the experiments: temperature with four levels (393.15, 423.15, 453.15, and 483.15 oK) and holding time with four levels (60, 120, 180, and 240 min). An artificial neural network (ANN) model was constructed to predict the hardness and impact toughness of precipitation-hardened 6061 aluminium alloy. The results revealed that the temperature, holding time, and interaction between them were significant factors on the hardness and impact toughness of Al 6061. ANN models' accuracy to predict the hardness and impact toughness of precipitation-hardened 6061 aluminium alloy was 99.1% and 97.6%, respectively. In this work, the ANN model accuracy was larger than ANOVA accuracy.