首页|Data on Machine Learning Described by a Researcher at University of Kassel (Predicting part quality early during an injection molding cycle)
Data on Machine Learning Described by a Researcher at University of Kassel (Predicting part quality early during an injection molding cycle)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – New study results on artificial intelligence have been published. According to newsreporting from Kassel, Germany, by NewsRx journalists, research stated, “Data-based process monitoringin injection molding plays an important role in compensating disturbances in the process and the associatedimpairment of part quality.”Our news reporters obtained a quote from the research from University of Kassel: “Selecting appropriatefeatures for a successful online quality prediction based on machine learning methods is crucial. Time seriessuch as the injection pressure and injection flow curve are particularly suitable for this purpose. Predictingquality as early as possible during a cycle has many advantages. In this paper it is shown how the recordinglength of the time series affects the prediction performance when using machine learning algorithms. For thispurpose, two successful molding quality prediction algorithms (k Nearest Neighbors and Ridge Regression)are trained with time series of different lengths on extensive data sets. Their prediction performances forpart weight and a geometric dimension are evaluated.”
University of KasselKasselGermanyEuropeCyborgsEmerging TechnologiesMachine Learning
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