Artificial Intelligence Develops High-Performance Alloys from Scrap Metal
Impurities in scrap metal are specifically utilized to improve ductility and tensile strength
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Worldwide, approximately two billion metric tons of metal are produced annually to meet the needs of industry, infrastructure, and the energy sector. A large portion of this is still extracted from ores. Conventional metal production accounts for approximately 40% of industrial greenhouse gas emissions and is responsible for about 10% of global energy consumption. Increased use of recycled metals could significantly reduce the environmental impact of primary production by keeping metals in the recycling loop longer, while also reducing dependence on imports of scarce and strategically important ores.
However, recycling metals into usable alloys poses significant challenges: the chemical composition of scrap metal often varies and contains unwanted impurities. These can degrade the quality of recycled alloys, rendering them unsuitable for demanding industrial applications.
An international research team has now developed a strategy based on artificial intelligence (AI) and machine learning to specifically utilize impurities in metal scrap as alloying elements. The resulting so-called “dirty alloys” not only tolerate variations in scrap composition but also exhibit improved mechanical properties and lower production costs. The findings were published in the journal *Science Advances*.
Artificial Intelligence Links Alloy Composition Directly to Material Properties
“Until now, impurities in metal scrap have typically been diluted by adding primary metal. However, this diminishes the environmental benefits of recycling, as the production of primary metals requires large amounts of energy and causes significant CO2 emissions. “Our new workflow uses AI and machine learning to identify suitable impurities and use them to develop alloys that not only tolerate impurities but actually benefit from them,” explains Dr. Liuliu Han, head of a partner group at the Max Planck Institute for Sustainable Materials and one of the corresponding authors of the publication.
What makes the new workflow unique is that it directly links an alloy’s chemical composition and manufacturing conditions to the desired material properties, without requiring extensive information about the alloy’s microstructure, but instead specifically leveraging the range of elements available from scrap to improve the product. Starting with already known alloys, a search algorithm generates new alloy candidates. To do this, it varies both the chemical composition and process parameters—such as temperature and duration of heat treatment—within specified limits.
Artificial neural networks then predict the tensile strength and elongation at break of the respective alloy candidates. This allows the algorithm to specifically search for compositions that offer a better balance between strength and formability than previously known materials. In doing so, it optimizes particularly promising alloys while also investigating compositions that have received little research attention to date. A second model, trained using a larger dataset on alloy compositions and material properties, evaluates the preselected candidates. The scientists then produce the most promising alloys and test them.
The results of these experiments are then fed back into the database. This updates the predictive model and guides the next round of optimization. Through this iterative comparison between computer-aided predictions and experimental results, the selection of suitable alloys gradually improves, accelerating the development of new materials from recycled metals.
Impurities Improve Mechanical Properties
The researchers used ductile iron, also known as spheroidal graphite cast iron, as the model material. This material is known for its high tolerance to impurities and is of great industrial importance: approximately 120 million metric tons of it are produced worldwide each year.
The research team examined 17 alloy variants proposed by the AI model. Of these, 14 achieved particularly good combinations of strength and ductility compared to the recycled iron materials previously recorded in the database.
With comparable elongation at break, tensile strength was increased by up to 15.2%. At comparable tensile strength, the elongation at break even increased by up to 73.2%. At the same time, production costs were about 8% lower than those of the conventional process, in which scrap metal is mixed with virgin iron.
The scientists also plan to apply their AI strategy to other material groups, such as recycled steels as well as aluminum and copper alloys. In addition, multiple AI agents are to take on different tasks and coordinate with one another—from developing new alloy compositions and designing experiments to controlling the process and evaluating the results. This would accelerate the development of new alloys while also enabling flexible adaptation to fluctuating scrap compositions.
Researchers from Shanghai Jiao Tong University, the City University of Hong Kong, East China University of Science and Technology, and Central South University in China, as well as the Max Planck Institute for Sustainable Materials in Germany, were involved in the work.
Note: This article has been translated using a computer system without human intervention. LUMITOS offers these automatic translations to present a wider range of current news. Since this article has been translated with automatic translation, it is possible that it contains errors in vocabulary, syntax or grammar. The original article in German can be found here.
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