首页|New Findings on Machine Learning from University of Augsburg Summarized (Fast Ap proximation of Fiber Reinforced Injection Molding Processes Using Eikonal Equati ons and Machine Learning)
New Findings on Machine Learning from University of Augsburg Summarized (Fast Ap proximation of Fiber Reinforced Injection Molding Processes Using Eikonal Equati ons and Machine Learning)
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Researchers detail new data in Machine Learning. According to news reporting from Augsburg, Germany, by NewsRx journal ists, research stated, "Injection molding is a popular production process for sh ort fiber reinforced components. The mechanical properties of such components de pend on process-induced fiber orientations which are commonly predicted via nume rical simulations." Funders for this research include Hightech Agenda Bavaria, Bavarian State Govern ment. The news correspondents obtained a quote from the research from the University o f Augsburg, "However, high computational costs prevent process simulations from being used in iterative procedures, such as topology optimization or finding opt imal injection locations. We propose a fast approximation method that extracts n odal features and train a regression model to predict fill states, cooling times , volumetric shrinkage, and fiber orientations. The features are determined by s olving eikonal equations with a fast iterative method and computing spatial mome nts to characterize node-adjacent material distributions. Subsequently, we use t hese features to train feed forward neural networks and gradient boosted regress ion trees with simulation data of a large dataset of geometries. This approach i s significantly faster than conventional methods, providing 20x speed-up for sin gle simulations and more than 200x speed-up in gate location optimization."
AugsburgGermanyEuropeCyborgsEmer ging TechnologiesMachine LearningUniversity of Augsburg
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