Effects of Detectable Defects (EDD) – Influence of production related defects in automated fiber placement processes in thin walled carbon fiber structures

可检测缺陷 (EDD) 的影响 â 薄壁碳纤维结构自动纤维铺放过程中生产相关缺陷的影响

• 批准号: 413627151
• 负责人: Professor Dr.-Ing. Berend Denkena
• 金额: --
• 依托单位: Institut für Flugzeugbau und Leichtbau
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2019
• 资助国家: 德国
• 起止时间: 2018-12-31 至 2021-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/413627151?language=en
• 关键词: Effects; Detectable; Defects; EDD; Influence

Automated Fiber Placement (AFP) is a well-established manufacturing process for the fabrication of high quality composite structures using pre-impregnated carbon fibers. The main research hypothesis proposes the possibility of performing a qualified structure-mechanical evaluation of the cured structure based on production defects in the uncured component. The necessary real time information to detect and classify production defects is provided by a thermographic process monitoring. This newly acquired knowledge describing the mechanical impact of production defects such as gap, overlap, fuzzball or twisted tow provides an unprecedented basis of decision-making on the type and necessity of corrective measures. Compared to conventionally methods of non-destructive failure analysis (e.g. ultrasonic testing, active infrared thermography, eddy current measurement, X-ray or computed tomography), it is possible to prove the defect during the production process. Therefore, the defect correction takes place much earlier and is less expen-sive. Furthermore, the current 100% ultrasonic testing rate after of the cured structures can be significantly reduced or replaced by a specific testing at critical points. This improvement in productivity and process reliability of the AFP technology leads to a significant increase in efficien-cy along the entire AFP process chain.The developed models to describe defects of a cured structure and the resulting mechanical properties based on data available during the production process exceed significantly the currently available process knowledge. The parametric modeling of different production-related defects and the associated experimental investigation of failure mechanisms generate knowledge about the influence of the curing process on production defects, while the thermal modeling of the AFP process as well as the development of machine learning algorithms characterizing and quantifying the production defects will generate further process knowledge.

自动化纤维放置（AFP）是一种完善的制造工艺，用于使用预先浸渍的碳纤维制造高质量的复合结构。主要研究假设提出了基于未固定成分中的生产缺陷对固化结构进行合格结构机械评估的可能性。热量表监测提供了检测和分类的必要实时信息来检测和分类生产缺陷。这种新获得的知识描述了生产缺陷的机械影响，例如差距，重叠，模糊或扭曲的拖车，为纠正措施的类型和必要性提供了前所未有的决策基础。与常规的非破坏性故障分析方法（例如，超声测试，主动红外热仪，涡流测量，X射线或计算机断层扫描）相比，可以在生产过程中证明缺陷。因此，缺陷校正发生得更早，并且支出较少。此外，当前100％的超声测试速率可以大大降低或用临界点处的特定测试降低或替换。 AFP技术的生产率和过程可靠性的这种提高导致沿整个AFP过程链的效率CY显着提高。开发的模型描述了基于生产过程中可用数据的数据的缺陷以及由此产生的机械性能，超过了当前可用的过程知识。不同生产相关缺陷的参数建模以及失败机制的相关实验研究产生了有关固化过程对生产缺陷的影响的知识，而AFP过程的热建模以及机器学习算法的发展表征和量化生产缺陷将产生进一步的过程知识。