Researchers have developed a technique to quickly determine certain properties of a material, like stress and strain, based on an image of the material showing its internal structure. The approach could one day eliminate the need for physics-based calculations, instead relying on computer vision and machine learning to generate estimates in real time. The advance could enable faster design prototyping and material inspections.

Calculations help reveal a material's internal forces, like stress and strain, which can cause that material to deform or break. Such calculations might suggest how a proposed bridge would hold up amid heavy traffic loads or high winds. The researchers used a machine learning technique called a Generative Adversarial Neural Network that was trained with thousands of paired images — one depicting a material's internal microstructure subject to mechanical forces and the other depicting that same material's color-coded stress and strain values. With these examples, the network uses principles of game theory to iteratively figure out the relationships between the geometry of a material and its resulting stresses.

The image-based approach is especially advantageous for complex composite materials. Forces on a material may operate differently at the atomic scale than at the macroscopic scale.

But the researcher's network is adept at dealing with multiple scales. It processes information through a series of “convolutions” that analyze the images at progressively larger scales.

The fully trained network successfully rendered stress and strain values given a series of close-up images of the microstructure of various soft composite materials. The network was even able to capture “singularities” like cracks developing in a material. In these instances, forces and fields change rapidly across tiny distances.

The advance could significantly reduce the iterations needed to design products. The end-to-end approach could have a significant impact on a variety of engineering applications, from composites used in the automotive and aircraft industries, to natural and engineered biomaterials.

In addition to saving engineers time and money, the new technique could give nonexperts access to state-of-the-art materials calculations. Product designers, for example, could test the viability of their ideas before passing the project along to an engineering team.

Once trained, the network runs almost instantaneously on consumer-grade computer processors. That could enable mechanics and inspectors to diagnose potential problems with machinery simply by taking a picture.

The researchers worked primarily with composite materials that included both soft and brittle components in a variety of random geometrical arrangements. In future work, they plan to use a wider range of material types.

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