Algorithm could shorten quality testing, research in many industries by months

A machine-learning algorithm developed at Sandia could provide auto manufacturing, aerospace and other industries a faster and more cost-efficient way to test bulk materials.

The technique was published recently in the scientific journal Materials Science and Engineering: A.

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, said Sandia scientist David Montes de Oca Zapiain, the lead author on the paper. However, these tests can take months to complete.

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