Researchers develop machine learning to make 3D printing smarter

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The following is excerpted from a report posted by the USC Viterbi School of Engineering.

3D printing often is touted as the future of manufacturing. But the technology experiences a high degree of error, such as distorting the shape of the part being printed.

Each printer is different, and the printed material can shrink and expand in unexpected ways. Manufacturers often need to try many iterations of a print before they get it right. What happens to the unusable print jobs? They must be discarded, presenting a significant environmental and financial cost to industry.A team of researchers from the USC Viterbi School of Engineering is tackling this problem with a new set of machine-learning algorithms and a software tool called PrintFixer. They expect the software will improve 3D printing accuracy by 50% or more, making the process vastly more economical and sustainable.

The objective of the team, led by Qiang Huang, associate professor of industrial and systems engineering, chemical engineering, and materials science, is to develop an AI model that accurately predicts shape deviations for all types of 3D printing.

“What we have demonstrated so far is that in printed examples, the accuracy can improve around 50% or more,” Huang said. “It can actually take industry eight iterative builds to get one part correct, for various reasons,” Huang said, “and this is for metal, so it’s very expensive.”

Every 3D-printed object results in some slight deviation from the design, whether this is due to printed material expanding or contracting when printed, or due to the way the printer behaves.

PrintFixer uses data gleaned from past 3D-printing jobs to train its AI to predict where the shape distortion will happen in order to fix print errors before they occur.