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By Mei Li, Carelyn Campbell, Katsuyo Thornton, Elizabeth A. Holm, Peter Gumbsch

This publication represents a suite of papers awarded on the 2d international Congress on built-in Computational fabrics Engineering (ICME), a forte convention geared up by way of The Minerals, Metals & fabrics Society (TMS), and held in 2013.

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The numerical modeling of quenching process is full of challenges, due to the complexity of part geometry and boundary conditions and in particular the variation of material behaviors under different thermal and stress conditions [5-6]. In this article, the numerical modeling of quenching, stretching and simplified machining processes of aluminum wrought alloys is demonstrated. In the transient thermal analysis, experimentally calibrated heat transfer coefficient (HTC) data has been applied to accurately predict temperature distribution at different time stages.

Microchemistry and microstructure alone do not affect the process very much although a slight variation in flow stress can be observed leading also to slight temperature variations. The change of the rolling speed however has a considerable effect. Shorter inter-stand times and increased work hardening at higher strain rates lead to an increase in exit temperature of 78°C. It is also interesting to notice, that the flow stress of the variant with lower solute content but higher amount of particles is initially higher but due to accelerated softening this difference diminishes slightly.

However, most of these approaches either have significant assumptions present or were developed for ideal cases, which significantly limits the ability to have a general model capable of predicting pit sizes for a variety of metallic systems and alloys that include many reactions and corrosion events. 31 Integrated Approach Being able to predict the maximum possible pit depth is important for determining product service and maintenance intervals. The objective of this effort is to develop an integrated set of modeling tools that can be used to identify pit growth criteria as a function of time, chemistry, and environment, which can also be linked with other life-prediction tools to determine product life.

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