High-strength aluminum alloys are critical for making cars and planes more lightweight and fuel-efficient, but manufacturers struggle to process them consistently. A University of Michigan-led team, in collaboration with General Motors Research & Development,  developed a computationally efficient, multiscale model that offers a predictive pathway to tune the chemistry and cooling of high strength aluminum alloys for optimized performance.

The ability to design less intensive manufacturing processes is critical to bringing lightweight aluminum alloys into broader use in automotive applications.

“This framework both improves our understanding of high-strength aluminum alloys and opens the door to modeling complex behaviors in many advanced alloys used for lightweight and sustainable manufacturing,” said MSE Associate Professor Liang Qi, a corresponding author of the study published in npj Computational Materials.

Because of the potential energy and safety advantages in expanding the use of lightweight metals, the study was funded by the National Science Foundation in collaboration with General Motors Research & Development.

“Ultimately, it gives researchers and engineers a powerful tool to design better materials more efficiently,” added Qi.

Simplifying processing for car manufacturing

The research team focused on 7000-series aluminum-magnesium-zinc (Al-Mg-Zn) alloys which were originally developed for aerospace applications. Tiny particles of magnesium and zinc, which substitute into the aluminum matrix, form precipitates that reinforce the aluminum to create exceptional strength at low weights.

However, the alloy has been limited to aerospace applications because its strengthening process—especially the natural aging step that occurs at room temperature—is highly unpredictable. Aerospace manufacturers avoid natural aging using costly, specialized processing steps like high-temperature deformation or low-temperature storage. While an effective workaround, these methods are unfavorable for large-scale vehicle manufacturing.

To reduce these costs and expand the material’s integration in autobody structures, the research team aims to understand the hardening processes on the microscale during natural aging—when the metal sits at room temperature over a period of time. 

“Our work helps engineers better understand how tiny defects and atomic movements affect the strengthening of advanced aluminum alloys, especially natural aging of Al-Mg-Zn alloys. The results of the research provide a pathway to understanding how to improve the formability of these alloys for automotive applications,” said Louis G. Hector Jr., a senior technical fellow at General Motors Research & Development and co-author of the study.

Processing strong, lightweight alloys

To strengthen Al-Mg-Zn alloys, the material is first heated to around 500 °C to ensure that all the zinc and magnesium atoms dissolve into the aluminum. Then, the alloy is rapidly cooled, a process known as quenching, and aged at room temperature for about one to three days.

The temperature drops so quickly during quenching that atoms do not have time to rearrange themselves to match the new equilibrium. This traps vacancies, which are missing atoms in the metal’s crystal structure. 

Trapped vacancies influence how fast clusters of zinc and magnesium atoms, called solutes, move during cooling steps. This, in turn, shapes where precipitates form and the resulting strength of the material.

Modeling vacancy diffusion in multicomponent alloys

Modeling vacancies and solute diffusion can help optimize performance, but this is extremely computationally expensive because it happens at the atomic scale over several days. 

At the atomic scale, vacancies hop between neighboring atoms and each hop depends on the local chemical environment. In alloys with three components, such as Al-Mg-Zn alloys, the number of possible solute configurations is enormous, which makes conventional atomistic simulations slow and unable to reach the timescales relevant for natural aging. 

Further, clusters can grow and evolve over seconds to hours, far beyond the reach of standard kinetic Monte Carlo methods for atomistic simulations.

To sidestep these issues, the researchers developed a multiscale framework that links atomic behavior to long-term aging kinetics.

A Markov chain model describes how long vacancies remain trapped inside clusters. Instead of modeling every atom, the model treats vacancy motion like a guided random walk through an energy landscape. These trapping behaviors are then fed into a mesoscale cluster-dynamics model that predicts how clusters evolve over hours of natural aging.

“We can now simulate hours or even days of material aging in just minutes, whereas traditional methods might struggle to simulate even a few seconds of real-time precipitate evolution,” said Zhucong Xi, an MSE postdoctoral fellow and lead author of the study.

Quench rate leaves a vacancy fingerprint

The multiscale framework found that quench rate leaves a long-lived “vacancy fingerprint” that controls natural aging.

Faster cooling leaves more mobile vacancies in the aluminum matrix, resulting in faster solute diffusion and cluster kinetics during natural aging. Slower cooling allows larger precipitates to form during quenching, which trap vacancies and extend the natural aging time frame. 

The model accurately predicts natural-aging behavior over days and shows how alloy chemistry, cooling and aging can be tuned to control the evolution of precipitates and, ultimately, the mechanical properties of aluminum alloys.

“By capturing vacancy–cluster interactions across length and time scales, we can now predict aging behavior that previously required intensive trial-and-error experimentation,” said Qi.

This research was funded by the National Science Foundation Grant Opportunities for Academic Liaison with Industry(190542).

Solute clusters kinetics during quenching and aging were observed at the Michigan Center for Materials Characterization, which is operated and maintained with support from indirect cost allocations in federal grants. 

--Story by Patricia Delacey, Michigan Engineering