An artificial intelligence approach extracts how an aluminum alloy's contents and manufacturing process are related to
specific mechanical properties.
Tsukuba, Japan, July 31, 2020 - (ACN Newswire) - Scientists in Japan have developed a machine learning approach that can
predict the elements and manufacturing processes needed to obtain an aluminum alloy with specific, desired mechanical
properties. The approach, published in the journal Science and Technology of Advanced Materials, could facilitate the
discovery of new materials.Aluminum alloys are lightweight, energy-saving materials which are used for various purposes, from welding materials for
buildings to bicycle frames. (Credit: Jozef Polc via123rf)
Aluminum alloys are lightweight, energy-saving materials made predominantly from aluminum, but also contain other
elements, such as magnesium, manganese, silicon, zinc and copper. The combination of elements and manufacturing process
determines how resilient the alloys are to various stresses. For example, 5000 series aluminum alloys contain magnesium
and several other elements and are used as a welding material in buildings, cars, and pressurized vessels. 7000 series
aluminum alloys contain zinc, and usually magnesium and copper, and are most commonly used in bicycle frames.
Experimenting with various combinations of elements and manufacturing processes to fabricate aluminum alloys is
time-consuming and expensive. To overcome this, Ryo Tamura and colleagues at Japan's National Institute for Materials
Science and Toyota Motor Corporation developed a materials informatics technique that feeds known data from aluminum
alloy databases into a machine learning model. This trains the model to understand relationships between alloys'
mechanical properties and the different elements they are made of, as well as the type of heat treatment applied during
manufacturing. Once the model is provided enough data, it can then predict what is required to manufacture a new alloy
with specific mechanical properties. All this without the need for input or supervision from a human.
The model found, for example, 5000 series aluminum alloys that are highly resistant to stress and deformation can be
made by increasing the manganese and magnesium content and reducing the aluminum content. "This sort of information
could be useful for developing new materials, including alloys, that meet the needs of industry," says Tamura.
The model employs a statistical method, called Markov chain Monte Carlo, which uses algorithms to obtain information and
then represent the results in graphs that facilitate the visualization of how the different variables relate. The
machine learning approach can be made more reliable by inputting a larger dataset during the training process.
Paper: https://doi.org/10.1080/14686996.2020.1791676About Science and Technology of Advanced Materials Journal
Open access journal STAM publishes outstanding research articles across all aspects of materials science, including
functional and structural materials, theoretical analyses, and properties of materials.