Prediction model of tensile strength of Al-Mg-Si alloy based on artificial neural network and genetic algorithm

Abstract:

In order to study the effect of heat treatment system and alloy composition on the mechanical properties of Al-Mg-Si alloys, the strength prediction model (ANN-GA model) of Al-Mg-Si alloys was constructed by using the combination of artificial neural network (ANN) and genetic algorithm (GA). The effects of alloying element content and heat treatment process parameters on the strength of aluminum alloy were studied by single factor and double factor analysis. The results show that the tensile strength of aluminum alloy decreases first and then increases with the increase of Si content; With the increase of Mg content, the increase of Cu content or the decrease of Fe content, the tensile strength of aluminum alloy increases as a whole. Two factor analysis can better reflect the influence of input parameters on the tensile strength of aluminum alloy. Mg/Si ratio, total amount of Mg+Si and aging time have significant effects on the mechanical properties of Al-Mg-Si alloys. The variation trend of hardness of aluminum alloy with time was consistent with the calculation results of ANN-GA model. The peak aging time was 29h and the relative error was 11.86%.

Key words:Al-Mg-Si alloy; Artificial neural networks; Genetic algorithm; Strength;