Abstract
In this paper, an artificial neural network (ANN) model with high accuracy and good generalization ability was developed to predict and optimize the mechanical properties of Al–7Si alloys. The quantitative correlation formulas of the mechanical properties with Mg content and heat treatment parameters were established based on the transfer function and weight values. The relative importance of the input variables, Mg content and heat treatment parameters, on the mechanical properties of Al–7Si alloys were identified through sensitivity analysis. The results indicated that the mechanical properties of Al–7Si alloys were sensitive to Mg content and aging temperature. Then the individual and the combined influences of these input variables on the properties of Al–7Si alloys were simulated and the process parameters were optimized using the artificial neural network model. Finally, the proposed model was validated to be a robust tool in predicting the mechanical properties of the Al–7Si alloy by conducting experiments.
Highlights
Al–7Si alloys are extensively used in aerospace and automotive industries due to their excellent formability, high corrosion resistance, and good comprehensive mechanical properties [1,2]
It has been verified that an optical artificial neural network should satisfy three important
It has been verified that an optical artificial neural network should satisfy three important conditions [30]: Compendious architecture, high fitting accuracy, and good generalization ability
Summary
Al–7Si alloys are extensively used in aerospace and automotive industries due to their excellent formability, high corrosion resistance, and good comprehensive mechanical properties [1,2]. The A356 and A357 alloys are two representative Al–7Si alloys with Mg contents ranging from 0.3 to. 0.7 wt.% [2,3,4] They are heat-treatable casting alloys, which can be strengthened by the precipitation of Mg2 Si after T6 treatment [5,6]. The size and morphology of the Mg–Si precipitates are controlled by the heat treatment process, including solution treatment, quenching, and artificial aging. An excellent strengthening effect benefits from the synergistic interaction between Mg content and heat treatment history
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