Search

The quenching processes of A357 aluminum alloy large complicated thin-wall workpieces were investigated by finite element method (FEM) simulation based on ABAQUS software. Heat transfer coefficients of different quenchants were accurately calculated by a traditional method of inverse heat transfer. The accurate heat transfer coefficients of different quenchants can ensure accuracy FEM results of temperature field of A357 alloy large complicated thin-wall workpieces during quenching. The quenchants of water, machine oil and 5%-UCON quenchant A were used. Quenching residual stress and distortion were investigated by considering the influence of quenchant and quenchant temperature. The maximum residual stress and distortion of quenched workpieces were predicted by using FEM simulation based on ABAQUS software.

Based on the computational fluid dynamics (CFD) method, a quenching tank with two agitator systems and two flow-equilibrating devices was selected to simulate flow distribution using Fluent software. A numerical example was used to testify the validity of the quenching tank model. In order to take tank parameters (agitation speed, position of directional flow baffle and coordinate position in quench zone) into account, an approach that combines the artificial neural network (ANN) with CFD method was developed to study the flow distribution in the quenching tank. The flow rate of the quenching medium shows a very good agreement between the ANN predicted results and the Fluent simulated data. Methods for the optimal design of the quenching tank can be used as technical support for industrial production.

The workpieces of A357 alloy were routinely heat treated to the T6 state in order to gain an adequate mechanical property. The mechanical properties of these workpieces depend mainly on solid-solution temperature, solid-solution time, artificial aging temperature and artificial aging time. An artificial neural network (ANN) model with a back-propagation (BP) algorithm was used to predict mechanical properties of A357 alloy, and the effects of heat treatment processes on mechanical behavior of this alloy were studied. The results show that this BP model is able to predict the mechanical properties with a high accuracy. This model was used to reflect the influence of heat treatments on the mechanical properties of A357 alloy. Isograms of ultimate tensile strength and elongation were drawn in the same picture, which are very helpful to understand the relationship among aging parameters, ultimate tensile strength and elongation.

Nanoindentation and high resolution electron backscatter diffraction (EBSD) were combined to examine the elastic modulus and hardness of α and β phases, anisotropy in residual elastic stress–strain fields and distributions of geometrically necessary dislocation (GND) density around the indentations within TA15 titanium alloy. The nano-indention tests were conducted on α and β phases, respectively. The residual stress–strain fields surrounding the indentation were calculated through cross-correlation method from recorded patterns. The GND density distribution around the indentation was calculated based on the strain gradient theories to reveal the micro-mechanism of plastic deformation. The results indicate that the elastic modulus and hardness for α phase are 129.05 GPas and 6.44 GPa, while for β phase, their values are 109.80 GPa and 4.29 GPa, respectively. The residual Mises stress distribution around the indentation is relatively heterogeneous and significantly influenced by neighboring soft β phase. The region with low residual stress around the indentation is accompanied with markedly high 〈 a 〉 type and prismatic-GND density.