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

自动纤维贴装(AFP)是一种成熟的制造工艺，用于使用预浸渍碳纤维制造高质量的复合材料结构。主要的研究假设提出了基于未固化部件中的生产缺陷对固化结构进行合格的结构-机械评估的可能性。通过热成像过程监控提供检测和分类生产缺陷所需的实时信息。这一新获得的知识描述了诸如缝隙、重叠、毛球或扭曲丝束等生产缺陷的机械影响，为制定纠正措施的类型和必要性提供了前所未有的决策基础。与传统的无损失效分析方法(如超声波检测、主动红外热成像、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，缺陷纠正发生得更早，成本也更低。此外，目前100%的超声探伤率在固化后可以显著降低或被临界点的特定检测所取代。AFP技术生产率和工艺可靠性的提高导致了整个AFP工艺链效率的显著提高。开发的基于生产过程中可用数据描述固化结构缺陷和由此产生的机械性能的模型大大超过了目前可用的工艺知识。不同生产相关缺陷的参数建模和相关的失效机制实验研究产生了关于固化过程对生产缺陷的影响的知识，而AFP过程的热建模以及表征和量化生产缺陷的机器学习算法的开发将产生进一步的工艺知识。

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.