Machine Learning makes metal 3D printing more efficient

An aerial view of the Peter the Great St. Petersburg Polytechnic University. Image via mun: planetRussian researchers have used machine learning to make metal 3D printing more efficient.

3D printers require fine tuning of positioning and control algorithms using mathematical models to reach optimal performance. This is a lengthy and arduous process and it could take weeks to set printing parameters. Even then, the possibility of printing error is always present.

To overcome such problems scientists at the Laboratory of Lightweight Materials and Structures of Peter the Great St. Petersburg Polytechnic University (SPbPU) have developed a neural network for a metal 3D printer.

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