The manufacturing process of carbon fiber reinforced polymer (CFRP) components is gaining a more and more significant role when looking at the increasing amount of CFRPs used in industries today. The monitoring of the manufacturing process and hence the reliability of the manufactured products, is one of the major challenges we need to face in the near future. Common defects which arise during manufacturing process are e.g. porosity and voids which may lead to delaminations during operation and under load. To find irregularities and classify them as possible defects in an early stage of the manufacturing process is of high importance for the safety and reliability of the finished products, as well as of significant impact from an economical point of view. In this study we compare various NDT methods which were applied to similar CFRP laminate samples in order to detect and characterize regions of defective volume. Besides ultrasound, thermography and eddy current, different X-ray methods like radiography, laminography and computed tomography are used to investigate the samples. These methods are compared with the intention to evaluate their capability to reliably detect and characterize defective volume. Beyond the detection and evaluation of defects, we also investigate possibilities to combine various NDT methods within a smart manufacturing process in which the decision which method shall be applied is inherent within the process. Is it possible to design an in-line or at-line testing process which can recognize defects reliably and reduce testing time and costs? This study aims to show up opportunities of designing a smart NDT process synchronized to the production based on the concepts of smart production (Industry 4.0). A set of defective CFRP laminate samples and different NDT methods were used to demonstrate how effective defects are recognized and how communication between interconnected NDT sensors and the manufacturing process could be organized.

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