One of the steps of the heat treatment process of age-hardenable aluminum alloys is the quenching process in which the alloy is cooled from the solutionizing temperature. The objective is to quench sufficiently fast to avoid undesirable concentration of alloying elements in the defect and grain boundary structure while at the same time not quenching faster than necessary to minimize residual stresses, which may lead to excessive distortion, or cracking. Various studies have been conducted to predict the relative quench rate sensitivity to yield different properties for age-hardenable alloys. Of these different predictive methods, the one that showed the more realistic results is quench factor analysis (QFA) since it involves a correlation of the cooling curve (time–temperature curve) of the cooling process throughout the quenching cycle for the desired cross-section size of interest with a C-curve (Time–Temperature–Property Curve) for the specific alloy of interest. The QFA numerical procedure has evolved since its original introduction. A review of the basic assumptions of the classical QFA model will be provided here, which will include discussion of the various improvements to the classical model that have been proposed over the intervening years since its introduction.

One of the steps of the heat treatment process of age hardenable aluminum alloys is the quenching process in which the alloy is cooled from the solutionizing temperature. The objective is to quench sufficiently fast to avoid undesirable concentration of alloying elements in the defect and grain boundary structure, while at the same time not quenching faster than necessary to minimize residual stresses, which may lead to excessive distortion or cracking. Various studies have been conducted to predict the relative quench rate sensitivity to yield different properties for age-hardenable alloys. Of these different predictive methods, the one that showed the more realistic results is quench factor analysis since it involves a correlation of the cooling curve (time-temperature curve) of the cooling process throughout the quenching cycle for the desired cross-section size of interest with a C-curve (time-temperature-property curve) for the specific alloy of interest. The quench factor analysis numerical procedure has evolved since its original introduction. A review of the basic assumptions of the classical quench factor analysis model will be provided here which will include discussion of the various improvements to the classical model that have been proposed over the intervening years since its introduction.

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Although cooling rate and strength correlations for a wide range of quenching conditions are routinely discussed, cooling time–temperature data are shown less often. However, intergranular corrosion is also cooling rate and pathway dependent, but such data correlation is much less likely to be encountered, especially by commercial quenchant suppliers. Even cooling rate data, strength, intergranular corrosion, and either residual stress or distortion correlations are more rarely reported together. This entry discusses the mechanism of intergranular corrosion and provides an illustrative example of the dependence of intergranular corrosion on the cooling rate.