Lucas Bogedale, Stephan Doerfel, Alexander Schrodt, Hans-Peter Heim
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Predicting part quality early during an injection molding cycle
Abstract Data-based process monitoring in injection molding plays an important role in compensating disturbances in the process and the associated impairment of part quality. Selecting appropriate features for a successful online quality prediction based on machine learning methods is crucial. Time series such as the injection pressure and injection flow curve are particularly suitable for this purpose. Predicting quality as early as possible during a cycle has many advantages. In this paper it is shown how the recording length of the time series affects the prediction performance when using machine learning algorithms. For this purpose, 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 for part weight and a geometric dimension are evaluated. The evaluations show that recording time series until the end of a cycle is not necessary to obtain good prediction results. These findings indicate that early reliable quality prediction is possible within a cycle, which speeds up prediction, allows timely part handling at the end of the cycle and provides the basis for automated corrective interventions within the same cycle.
期刊介绍:
International Polymer Processing offers original research contributions, invited review papers and recent technological developments in processing thermoplastics, thermosets, elastomers and fibers as well as polymer reaction engineering. For more than 25 years International Polymer Processing, the journal of the Polymer Processing Society, provides strictly peer-reviewed, high-quality articles and rapid communications from the leading experts around the world.