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An Approach for the Sensory Integration into the Automated Production of Carbon Fiber Reinforced Plastics

机译:感觉整合到碳纤维增强塑料自动生产中的一种方法

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The advantages of Carbon fiber reinforced composites (CFRC) lead to an increasing demand of Carbon fiber products. This class of materials is gaining widespread acceptance in various fields like aviation, wind energy or automotive and is gradually replacing traditional lightweight construction materials such as high-strength steel or aluminum. Currently, particular process steps of the production of fiber composite structures are performed manually or semi-automatically. Especially the automated handling of semi-finished products consisting unstable textile poses a challenge for an economical manufacturing. The reasons for the missing automation are beside the lacking technical feasibility most of all, reliability issues during process execution, representing key aspects for potential large-scale production. As a consequence, the integration of sensor systems constitutes a promising approach for process optimization and quality assurance. In order to catch the intricate nature of possible defects and their interdependences during the single steps of the handling process, an approach for selecting, assembling and integrating the ideal sensors at the respective processing station to monitor dominant defects is presented. For this purpose, possible defects and flaws are derived from a comprehensive process analysis and accordingly suitable sensor principles are selected. The application of this approach is exemplarily demonstrated on an automotive case study focusing the separating and draping steps of flat carbon fiber textiles in a mold for a resin transfer molding (RTM) process.
机译:碳纤维增强复合材料(CFRC)的优势导致对碳纤维产品的需求增加。这类材料已在航空,风能或汽车等各个领域获得广泛认可,并正在逐步取代传统的轻质建筑材料,例如高强度钢或铝。当前,纤维复合结构的生产的特定工艺步骤是手动或半自动进行的。尤其是由不稳定的纺织品组成的半成品的自动化处理对于经济的制造提出了挑战。缺少自动化的原因除了缺乏技术可行性外,最主要的是过程执行过程中的可靠性问题,这代表了潜在的大规模生产的关键方面。结果,传感器系统的集成构成了用于过程优化和质量保证的有前途的方法。为了在处理过程的单个步骤中捕捉可能的缺陷及其相互依存关系的复杂性质,提出了一种在各个处理站选择,组装和集成理想传感器以监控主要缺陷的方法。为此,从全面的过程分析中得出可能的缺陷和缺陷,并据此选择合适的传感器原理。这种方法的应用已在汽车案例研究中得到了示范,该案例研究集中在树脂传递模塑(RTM)工艺的模具中扁平碳纤维织物的分离和悬垂步骤。

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