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Transfer Learning for Optimization of Carbon Fiber Reinforced Polymer Production


Transfer Learning for Optimization of Carbon Fiber Reinforced Polymer Production

The main problem that keeps many areas of research from using Deep Learning methods is the lack of sufficient amounts of data. We propose transfer learning from simulated data as a solution to that issue. In this work we present the industrial use case for which we plan to apply our transfer learning approach to: The production of economic Carbon Fiber Reinforced Polymer components. It is currently common to draw samples of produced components statistically and perform a destructive test on them, which is very costly. Our goal is to predict the quality of components during the production process. This has the advantage of enabling not only on-line monitoring but also adaptively optimizing the manufacturing procedure. The data comes from sensors embedded in a Resin Transfer Molding press.
published 2018 Organic Computing: Doctoral Dissertation Colloquium 2018

Publisher: kassel university press GmbH