Material selection or specification is an extremely important part of the design phase that will go a long way toward ultimately determining the long-term success of engineering applications.
Materials for a design application could include metals, alloys polymers, composites, ceramics or glass. When choosing the best material to use for your application, there are many things to consider, including:
- What is the desired appearance, texture or color of the material?
- Does the material need to be conductive or resistive to electrical or thermal conductivity?
- Does the material need to be strong, lightweight or a combination of the two?
Sometimes however, there are factors that limit material selection. Availability or manufacturability of the material could mean the difference between the ability to select the best material for the application or having to choose the next best option. What if there was another choice, though? What if instead of choosing a material, you could specify the properties that you needed in a material and then automatically produce that material with those desired properties.
Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory recently developed a new system demonstrating that the design of microstructures is now more than just scientific theory. With this new system, designers can numerically specify the properties they want materials to have, and the system generates a microstructure that matches those specifications.“We did it for relatively simple mechanical properties, but you can apply it to more complex mechanical properties, or you could apply it to combinations of thermal, mechanical, optical, and electromagnetic properties,” Matusik said. “Basically, this is a completely automated process for discovering optimal structure families for metamaterials.”
In today’s world of modern manufacturing, complex microstructures are often used, although this has typically been performed by hand by extremely experiences designers. The press release notes that “for decades, materials scientists have taken inspiration from the natural world. They’ll identify a biological material that has some desirable trait—such as the toughness of bones or conch shells—and reverse-engineer it. Then, once they’ve determined the material’s “microstructure,” they’ll try to approximate it in human-made materials.”
Now however, using the new algorithm, the research team is able to automatically discover new microstructure families with the best trade-offs between the material properties. The first step in this process is for the team to specify the base materials and their properties, such as a solid that will fill a particular void. From there, the algorithm computes all the possible microstructures and their corresponding mechanical properties based on the initial selection. Then it discovers the best-performing microstructure families in three steps: gamut expansion, family identification and template construction. Users can fine-tune the results and print a 3D model that matches the algorithm’s simulation. Watch this video to learn more about designing printable microstructures.
While this is still a long way off from the sci-fi fantasy of Star Trek replicators, it is still a huge breakthrough—and one of the most intriguing aspects of the process is that it’s entirely automated. Matusik explained that every step in this process is completely automated, including the measurement of similarities, the clustering, the skeleton extraction, the formula derivation, and the correlation of geometries and properties. He also noted that this approach could also be applied to any collection of microstructures evaluated according to any criteria.
Read about future uses of customized materials in Making Smart Devices Smarter with Novel Materials.