This project will study the nucleation of precipitates in high-strength aluminum (Al) alloys to facilitate their application in the automobile industry. Alloying elements called 'solutes' in metallic materials can exist in different forms, or phases. The solutes are in solid solution phases if they are randomly distributed in alloys as individual atoms; they can also be in certain small particles or 'precipitate' phases if some solutes agglomerate together to form specific structures through typical nucleation and growth progresses. Mechanical properties of alloys depend on the phases of solutes. Generally, alloys in solid solution phases are soft and ductile, convenient for low-cost mechanical forming and fabrication; but alloys with certain precipitate phases can be much harder and, therefore, difficult for mechanical forming compared with their counterparts in solid solution phases, so they are usually achieved in alloy components at final product stages.
7000 series aluminum alloys with zinc (Zn) and magnesium (Mg) as the major alloying elements have a high strength-to-weight ratio after proper precipitation hardening (similar strength but half of the weight compared with conventional steel). Their widespread implementation in the automotive industry as structural components can achieve vehicles with lightweight and high fuel efficiency. However, the solute solution phases in these alloys can quickly (~30 minutes) transform into precipitate phases to harden the alloys even at room temperature, making significant challenges of their low-cost forming and fabrication using current automobile manufacturing techniques.
This University of Michigan-General Motors collaborative GOALI project aims to apply an integrated computational, experimental and statistical approach to understand and control the early stages of solid-solution-to-precipitate transformation kinetics of 7000 series aluminum alloys. The key objective is to design new alloy chemistries to retard the nucleation and growth of early-stage precipitate phases at room temperatures. These early-stage precipitates are mainly solute clusters and Guinier-Preston (GP) zones, both of which are made of a small number (less than 1000) of solute atoms agglomerated together. Then these alloys can stay in soft solid-solution phases for a longer time, convenient for conventional automobile manufacturing techniques. In addition, the new alloy chemistries should not impede the final precipitation hardening at a higher temperature. The proposed research will potentially enable implementation of 7000 series aluminum alloys in the automobile industry, contributing to vehicle light-weighting and favorably impacting energy savings, sustainability, and competitiveness. The generated computational-experimental-statistical framework and new knowledge will be applicable to alloy design in general and thus accelerate material development for meeting future needs. The proposed teaching and training elements will enable an integrated-computational-materials-engineering (ICME) approach to be widely imparted to senior undergraduate and graduate students in materials major and champion outreach/education activities of K-12 students as well as opportunities for students of underrepresented groups to be engaged in start-of-the-art materials research.
Nucleation and growth theories of precipitates in solids are key fundamental principles to guide the development and application of advanced age-hardenable structural alloys. However, the conventional theories fail to provide quantitative guidance for the development and processing of multicomponent commercial alloys, where nucleation and growth of precipitates can occur in multiple steps with substantial structural-composition transformations under the influence of defects. This gap between theory and practice limits the industrial applications of many commercial alloys that require special fabrication and manufacturing processes. For example, high-strength Al-Zn-Mg-based 7000 series alloys have severe formability limitations if stamped more than ~30 minutes after the solutionizing treatment. These limitations result from fast precipitate kinetics at room temperature ('natural aging'), mainly the nucleation and growth of solute clusters and Guinier-Preston (GP) zones that can act as nuclei for subsequent precipitates. Understanding and controlling these nucleation and growth processes can slow down natural aging, and thereby expand the room temperature forming window amenable to the sustainable manufacturing of 7000 series Al alloys and other lightweight high-strength materials in the automobile industry, which has a significant impact on vehicle mass reduction.
In this industry-university collaborative GOALI project, the applicants plan to apply an integrated theoretical, computational, experimental and machine learning approach to understand and control the nucleation and growth kinetics of solute clusters and GP zones in Al-Zn-Mg-based alloys. A multi-scale simulation framework based on first-principles calculations, atomistic simulations, and phenomenological hardening model will be constructed to quantitatively describe the solute clusters and GP zone kinetics and their effects on hardness increments. Alloys with the proposed solutes will be synthesized and subjected to thermal processing and indentation hardness tests to verify their natural aging kinetics. A combination of high-resolution transmission electron microscopy, electron energy loss spectroscopy and computer image simulations will be used to characterize the solute clusters and fine precipitates to verify the nucleation and growth mechanisms. A statistical machine learning surrogate model will be constructed to speed up the search of alloy chemistries to retard natural aging with further experimental confirmations.
In this project, the research team proposes a transformative alloy design concept to tune the early-stage precipitation kinetics of complex commercial alloys by searching the trace solute elements to control the structures and compositions of solid clusters and GP zones beyond the role of individual atoms of trace solute elements. The research team also proposes an efficient routine to design advanced alloys with a large parameter space by applying the integrated computational, experimental and statistical machine learning methods. Quantitative understanding of precipitate nucleation and growth kinetics in 7000 series Al alloys using the newly developing computational and experimental tools will facilitate the development of advanced nucleation and growth theories for generalized multicomponent alloy systems.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.