Modelling of the Age-Hardening Behavior in AA6xxx within a Through-Process Modelling Framework

Abstract

The manufacturing of AA6xxx car body panels typically consists of rolling, ageing and forming processes. Thus, multiple simulation tools can be coupled to set up a through-process modelling (TPM) framework for predicting the evolution of microstructure and the final mechanical properties of these products. In order to realize such a TPM concept, various industrial processing phenomena were studied and modelled in the open innovation research cluster “Advanced Metals and Processes” (AMAP♯). This work focuses on the age hardening behavior which takes place during the industrial paint bake process. To reflect the microstructure evolution of this processing step, a multi-component precipitation model is developed. So far, the influences of thermomechanical processes, i.e. annealing temperature on the kinetics of MgxSiy precipitates during artificial aging were implemented. The precipitation model was linked to a yield strength model in order to simulate the evolution of mechanical properties within the TPM framework. For validation, the evolution of microstructure and mechanical properties of an AA6016 alloy during artificial ageing was investigated via transmission electron microscopy (TEM) and tensile testing. The simulation results are in agreement with experimental observations.

Phase Field Simulation of AA6XXX Aluminium Alloys Heat Treatment

“…On the contrary, diffusion coefficients of Mg and Si in the matrix phase are increased for A posteriori, Equation ( 10) is implemented, taken the composition of Mn kai Fe from Table 1, so as to identify if the absence of iron particles resulted in an overestimation of available Si, and consequently, an overestimation of precipitate fraction and radius. Figure 12 presents the availability of Si for precipitation based on the restriction of Equation (10). As anticipated, Mg and Si concentrations in the matrix decreased, as the volume fraction of precipitates increased, with the most significant reduction estimated in the 200 • C ageing simulation, where the highest value of precipitates is recorded.…”

“…Mg and Si concentrations in Mg 2 Si are constant, 63% and 37%, respectively. The conducted simulations do not consider the iron intermetallic particles, which restrict the available Si, based on Equation (10), as long as the presence of iron intermetallic phases reduces the available Si for the Mg 2 Si precipitation. Figure 13 presents the microstructure simulation by the end of each ageing simulation.…”

“…For the isothermal artificial ageing process, numerous models have been proposed, considering the alloying elements concentration and the ageing conditions (time and temperature) in order to predict the precipitation volume fraction and radius, and their contribution to the overall strengthening of the alloy. These models lie on both the atomistic scale, as the Kinetic Monte Carlo approach [8], and the mesoscale, like the Kampman-Wagner [9] and the KiNG model [10], while the thermodynamic approach of CALPHAD has also been taken into account [11].…”

“…Mg and Si concentrations in Mg2Si are constant, 63% and 37%, respectively. The conducted simulations do not consider the iron intermetallic particles, which restrict the available Si, based on Equation(10), as long as the presence of iron intermetallic phases reduces the available Si for the Mg2Si precipitation.Α posteriori, Equation (10) is implemented, taken the composition of Mn kai Fe from Table1, so as to identify if the absence of iron particles resulted in an overestimation of available Si, and consequently, an overestimation of precipitate fraction and radius. Fig-…”

Integrated property predictions during casting and heat treatment of Al-Si-Cu-Mg alloys by precipitation simulation

“…Intensive works have been made by applying different modelling methods to simulate precipitation and microstructure evolution in aluminum alloys. Among the various methods, classical nucleation and growth theory (CNGT) based approaches has rendered itself as an excellent option owing to its accuracy and computational performance in predicting particle growth in various aluminum alloys [4,[9][10][11][12][13]. In this study, a CNGT based kinetic precipitation model has been developed to simulate the dissolution and formation of particles during solution treatment, quenching, and artificial ageing treatment of Al-Si-Cu-Mg alloys.…”