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Machine Learning to Predict Direction-dependent Mechanical Properties of Metals

Production stoppages are costly so manufacturers screen materials like sheet metal for formability before using them to make sure the material will not crack when it is stamped, stretched and strained as it’s formed into different parts. Companies often use commercial simulation software calibrated to the results of various mechanical tests. However, these tests can take months to complete.

While certain high-fidelity computer simulations can assess formability in only a few weeks, companies need access to a supercomputer and specialized expertise to run them. Sandia National Laboratories has shown machine learning can dramatically cut time and resources to calibrate commercial software because the algorithm does not need information from mechanical tests. Nor does the method need a supercomputer. Additionally, it opens a new path to perform faster research and development.

The machine-learning algorithm named MAD3, short for Material Data Driven Design, works because metal alloys are made of microscopic, so-called “crystallographic” grains. Collectively, these grains form a texture that makes the metal stronger in some directions than others, a phenomenon that researchers call mechanical anisotropy.

We’ve trained the model to understand the relationship between crystallographic texture and anisotropic mechanical response. You need an electron microscope to get the texture of metal, but then you can drop that information into the algorithm, and it predicts the data you need for the simulation software without performing any mechanical tests.

– David Montes de Oca Zapiain, Sandia Scientist

Teaming with Ohio State University, Sandia trained the algorithm on the results of 54,000 simulated materials tests using a technique called a feed-forward neural network. The Sandia team then presented the algorithm with 20,000 new microstructures to test its accuracy, comparing the algorithm’s calculations with data gathered from experiments and supercomputer-based simulations.

The developed algorithm is about 1,000 times faster compared to high-fidelity simulations. We are actively working on improving the model by incorporating advanced features to capture the evolution of the anisotropy since that is necessary to accurately predict the fracture limits of the material.

As a national security laboratory, Sandia is conducting further research to explore whether the algorithm can shorten quality assurance processes for the U.S. nuclear stockpile, where materials must meet rigorous standards before being accepted for production use. The National Nuclear Security Administration funded the machine-learning research through the Advanced Simulation and Computing program.

To enable other institutions to take advantage of the technology, Sandia formed a cross-disciplinary team to develop the user-friendly, graphics-based Material Data Driven Design software. It was developed with input from more than 75 interviews with potential users through the Department of Energy’s Energy I-Corps program.

As reported by OpenGov Asia, Sandia National Laboratories researchers have created a method of processing 3D images for computer simulations that could have beneficial implications for several industries, including health care, manufacturing and electric vehicles.

The researchers shared the new workflow, dubbed by the team as EQUIPS for Efficient Quantification of Uncertainty in Image-based Physics Simulation. The lead author of the paper said that this workflow leads to more reliable results by exploring the effect that ambiguous object boundaries in a scanned image have in simulations. EQUIPS can use machine learning to quantify the uncertainty in how an image is drawn for 3D computer simulations. By giving a range of uncertainty, the workflow allows decision-makers to consider best- and worst-case outcomes.

Using the EQUIPS workflow, which can use machine learning to automate the drawing process, the 3D image is rendered into many viable variations showing the size and location of a potential tumour. Those different renderings will produce a range of different simulation outcomes. Instead of one answer, the doctor will have a range of prognoses to consider that can affect risk assessments and treatment decisions, be they chemotherapy or surgery.

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