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Defect recognition in CFRP components using various NDT methods within a smart manufacturing process
(2018)
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.
Photon counting detectors (PCD) offer new possibilities for x-ray micro computed tomography (CT) in the field of non-destructive testing. For large and/or dense objects with high atomic numbers the problem of scattered radiation and beam hardening severely influences the image quality. This work shows that using an energy discriminating PCD based on CdTe allows to address these problems by intrinsically reducing both the influence of scattering and beam hardening.
Based on 2D-radiographic measurements it is shown that by energy thresholding the influence of scattered radiation can be reduced by up to in case of a PCD compared to a conventional energy-integrating detector (EID).
To demonstrate the capabilities of a PCD in reducing beam hardening, cupping artefacts are analyzed quantitatively. The PCD results show that the higher the energy threshold is set, the lower the cupping effect emerges. But since numerous beam hardening correction algorithms exist, the results of the PCD are compared to EID results corrected by common techniques. Nevertheless, the highest energy thresholds yield lower cupping artefacts than any of the applied correction algorithms.
As an example of a potential industrial CT application, a turbine blade is investigated by CT. The inner structure of the turbine blade allows for comparing the image quality between PCD and EID in terms of absolute contrast, as well as normalized signal-to-noise and contrast-to-noise ratio. Where the absolute contrast can be improved by raising the energy thresholds of the PCD, it is found that due to lower statistics the normalized contrast-to-noise-ratio could not be improved compared to the EID. These results might change to the contrary when discarding pre-filtering of the x-ray spectra and thus allowing more low-energy photons to reach the detectors.
Despite still being in the early phase in technological progress, PCDs already allow to improve CT image quality compared to conventional detectors in terms of scatter and beam hardening reduction.
A composite material is a combination of two or more materials with very different mechanical, thermal and electrical properties. The various forms of composite materials, due to their high material properties, are widely used as structural materials in the aviation, space, marine, automobile, and sports industries. However, some defects like voids, delamination, or inhomogeneous fiber distribution that form during the fabricating processes of composites can seriously affect the mechanical properties of the composite material. In this study, several imaging NDE techniques such as: thermography, high frequency eddy current, ultrasonic, x-ray radiography, x-ray laminography, and high resolution x-ray CT were conducted to characterize the microstructure of carbon fiber composites. Then, a 3D analysis was implemented by the destructive technique of serial sectioning for the same sample tested by the NDE methods. To better analyze the results of this work and extract a clear volume image for all features and defects contained in the composite material, an intensive comparison was conducted among hundreds of 3D-NDE and multi serial sections’ scan images showing the microstructure variation.
Defect recognition in CFRP components using various NDT methods within a smart manufacturing process
(2017)
The manufacturing process of carbon fiber reinforced polymer (CFRP) components is playing 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 are e.g. porosity and voids which may lead to delaminations during operation and under load. To find those defects in an early stage of the manufacturing process is of huge 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 present various NDT methods which are applied to similar CFRP laminate samples in order to detect and characterize 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 of flaws, we also investigate possibilities to combine various NDT methods within a smart manufacturing process in which the decision which method to apply is made by the process itself. 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 ideas 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.