Applying Machine Learning for Real Time Optimization of Powder Bed Manufacturing Public
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Despite continuous growth and improvements in Selective Laser Melting (SLM) systems, part quality and reproducibility are still affected by process instability. The aim of this project is to illustrate improvement in quality and consistency of SLM printed parts by introducing machine learning. In order to achieve this, we set out to build an SLM testbed system with integrated sensing capabilities, and utilize machine learning and in-situ process monitoring to introduce delayed, closed-loop sensing and control to the SLM process.
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Permanent link to this page: https://digital.wpi.edu/show/2514np09z