Materials Science

Assembly Line

These autonomous factories on satellites will produce materials in space that can’t be made on Earth

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Topics: materials science, autonomous factory

Organizations: Space Forge

Bacon and cofounder-CEO Joshua Western want to take advantage of the unique conditions in space—the very low gravity and the fact that it’s an almost perfect vacuum—to make materials that can’t be made on Earth. Some new materials have already been produced on the International Space Station. A new type of fiber-optic cable, for example, is cloudy when it’s made on Earth because of gravity and impurities in the air, but crystal clear when made in space.

In space, it’s possible to manufacture new alloys that can be used to make bigger, stronger, turbines on aircraft, so planes use less fuel. On electric planes, new materials can make the electronic connections between batteries and the propeller motor more efficient, so the planes need less cooling equipment and can carry more passengers. Space factories are also well-suited to make better batteries for electric planes or cars. Wind turbines, for example, are more efficient the larger they are, but have to be made in pieces so they can be transported to a site for installation, and then held together with bolts. By making bolts that are stronger than what can be manufactured on Earth, it’s possible to develop a larger, more efficient wind turbine that can create more energy.

Read more at Fast Company

Machine-learning system accelerates discovery of new materials for 3D printing

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Author: Adam Zewe

Topics: materials science, 3d printing, additive manufacturing

Organizations: MIT

The growing popularity of 3D printing for manufacturing all sorts of items, from customized medical devices to affordable homes, has created more demand for new 3D printing materials designed for very specific uses.

A material developer selects a few ingredients, inputs details on their chemical compositions into the algorithm, and defines the mechanical properties the new material should have. Then the algorithm increases and decreases the amounts of those components (like turning knobs on an amplifier) and checks how each formula affects the material’s properties, before arriving at the ideal combination.

The researchers have created a free, open-source materials optimization platform called AutoOED that incorporates the same optimization algorithm. AutoOED is a full software package that also allows researchers to conduct their own optimization.

Read more at Phys.org

Machine learning predictions of superalloy microstructure

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Authors: Patrick L Taylor, Gareth Conduit

Topics: machine learning, materials science

Organizations: University of Cambridge, Intellegens

Gaussian process regression machine learning with a physically-informed kernel is used to model the phase compositions of nickel-base superalloys. The model delivers good predictions for laboratory and commercial superalloys. Additionally, the model predicts the phase composition with uncertainties unlike the traditional CALPHAD method.

Read more at ScienceDirect

Complex machine validations performed with multiphysics simulation

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Author: Rahul Garg

Topics: digital twin, materials science

Vertical: Machinery

Organizations: Siemens

When new materials and methods are applied to manufacturing, it increases product complexity. But the benefits can be significant: Products are now lighter, smaller and more easily customizable to meet consumer demands. Multiphysics simulations enable machine builders to explore the physical interactions complex products encounter, virtually. It tracks interactive data of product performance, safety and longevity.

Read more at Plant Engineering

Using AI to Find Essential Battery Materials

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Author: @mariagallucci

Topics: AI, materials science

Vertical: Mining

Organizations: KoBold Metals, IBM, IEEE

KoBold’s AI-driven approach begins with its data platform, which stores all available forms of information about a particular area, including soil samples, satellite-based hyperspectral imaging, and century-old handwritten drilling reports. The company then applies machine learning methods to make predictions about the location of compositional anomalies—that is, unusually high concentrations of ore bodies in the Earth’s subsurface.

Read more at IEEE Spectrum

Leveraging AI and Statistical Methods to Improve Flame Spray Pyrolysis

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Author: Stephen J. Mraz

Topics: AI, machine learning, materials science

Vertical: Chemical

Organizations: Argonne National Laboratory

Flame spray pyrolysis has long been used to make small particles that can be used as paint pigments. Now, researchers at Argonne National Laboratory are refining the process to make smaller, nano-sized particles of various materials that can make nano-powders for low-cobalt battery cathodes, solid state electrolytes and platinum/titanium dioxide catalysts for turning biomass into fuel.

Read more at Machine Design