Towards Real-time Process Monitoring and Machine Learning for Manufacturing Composite Structures
| Autor: | Alexander Schiendorfer, Alwin Hoffmann, Simon Stieber, Jan Faber, Michaela Richter, Matthias Beyrle, Markus G. R. Sause, Wolfgang Reif |
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| Rok vydání: | 2020 |
| Předmět: |
Thermoplastic
Transfer molding Computer science Automotive industry Carbon fibers Stability (learning theory) Thermosetting polymer 02 engineering and technology Machine learning computer.software_genre 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine ddc:530 Aerospace chemistry.chemical_classification Thermoplastic materials business.industry Process (computing) 021001 nanoscience & nanotechnology Material flow chemistry visual_art Polyamide visual_art.visual_art_medium Artificial intelligence 0210 nano-technology business computer |
| Zdroj: | ETFA |
| DOI: | 10.1109/etfa46521.2020.9212097 |
| Popis: | Components made from carbon fiber reinforced plastics (CFRP) offer attractive stability properties for the automotive or aerospace industry despite their light weight. To automate CFRP production, resin transfer molding (RTM) based on thermoset plastics is commonly applied. However, this manufacturing process has its shortcomings in quality and costs. The project CosiMo aims for a highly automated and cost-attractive manufacturing process using cheaper thermoplastic materials. In a thermoplastic RTM (T-RTM) process, the polymerization of ϵ-caprolactam to polyamide 6 is investigated using an intelligent mold tooling. Multiple sensor types integrated into the mold allow for tracking of process-relevant variables, such as material flow and polymerization state. In addition to monitoring the T-RTM process, a digital twin visualizes progress and makes predictions about issues and countermeasures based on machine learning. |
| Databáze: | OpenAIRE |
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