首页|University of Leuven (KU Leuven) Details Findings in Machine Learning (Photodiod e-based Porosity Prediction In Laser Powder Bed Fusion Considering Inter-hatch a nd Inter-layer Effects)
University of Leuven (KU Leuven) Details Findings in Machine Learning (Photodiod e-based Porosity Prediction In Laser Powder Bed Fusion Considering Inter-hatch a nd Inter-layer Effects)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on Machine Learning have been presented. According to news reporting originating from Heverlee, Belgium, by NewsRx correspondents, research stated, “Laser powder bed fusion, while prom ising, faces hurdles in certifying fabricated parts due to cost and complexity, with in-process monitoring emerging as a potential solution. Existing models foc us on predicting defects at a given location using the monitoring signals from s olely that same location.” Financial supporters for this research include European Union (EU), Marie Curie Actions.
HeverleeBelgiumEuropeCyborgsEmer ging TechnologiesMachine LearningUniversity of Leuven (KU Leuven)
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