Analytische Chemie
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This article focuses on the fusion of flaw indications from multi-sensor nondestructive materials testing. Because each testing method makes use of a different physical principle, a multi-method approach has the potential of effectively differentiating actual defect indications from the many false alarms, thus enhancing detection reliability. In this study, we propose a new technique for aggregating scattered two- or three-dimensional sensory data. Using a density-based approach, the proposed method explicitly addresses localization uncertainties such as registration errors. This feature marks one of the major of advantages of this approach over pixel-based image fusion techniques. We provide guidelines on how to set all the key parameters and demonstrate the technique's robustness. Finally, we apply our fusion approach to experimental data and demonstrate its capability to locate small defects by substantially reducing false alarms under conditions where no single-sensor method is adequate.
The characterisation of AM structures is an important aspect of the AM process, required in order to:
1. optimise the AM printing process
2. assess the quality of produced parts
A wide range of characterisation techniques are available, and the selection can be complex, based on multiple factors.
One output from the MetAMMi project is a good practice guide on the correct choice of characterisation technique.
The necessity and demand for nondestructive testing of wood-based materials which can automatically scan huge areas of wood is increasing. Air-coupled ultrasound (ACU) is used to detect defects and damage without altering the structure permanently. Using through transmission it is possible to detect even small holes and missing adhesive. If only one side of an object is accessible the reflection mode is preferred at the expense of a reduced resolution and penetration depth. Novel ferroelectret transducers with a high signal-to-noise ratio (SNR) enable a high-precision structure recognition. The transducers made of cellular polypropylene (PP) are quite suitable for ACU testing due to their extremely low Young’s modulus and low density which result in a favorable acoustic impedance for the transmission of ultrasonic waves between the transducer and air. Thus, defects such as delamination, rot, and cracks can be detected. Promising results were obtained under laboratory conditions with frequencies from 90 kHz to 200 kHz. The advantage of these ACU transducers is that they do not require contact to the sample, are accurate, and cost effective. Ultrasonic quality assurance for Wood is an important attempt to increase the acceptance of wooden structures and towards sustainability in civil engineering in general.
The necessity and demand for nondestructive testing of wood-based materials which can
automatically scan huge areas of wood is increasing. Air-coupled ultrasound (ACU) is used to detect defects and damage without altering the structure permanently. Using through transmission it is possible to detect even small holes and missing adhesive. If only one side of an object is accessible the reflection mode is preferred at the expense of a reduced resolution and penetration depth. Novel ferroelectret transducers with a high signal-to-noise ratio (SNR) enable a high-precision structure recognition. The transducers made of cellular polypropylene (PP) are quite suitable for ACU testing due to their extremely low Young’s modulus and low density which result in a favorable acoustic impedance for the transmission of ultrasonic waves between the transducer and air. Thus, defects such as delamination, rot, and cracks can be detected. Promising results were obtained under laboratory conditions with frequencies from 90 kHz to 200 kHz. The advantage of these ACU transducers is that they do not require contact to the sample, are accurate, and cost effective. Ultrasonic quality assurance for wood is an important attempt to increase the acceptance of wooden structures and towards sustainability in civil engineering in general.
This study investigates the non-destructive detection of delaminations in concrete plates using non-contact laser ablation, instead of the conventional hammer excitation, as part of the impact echo method. We performed tests on five concrete specimens of different sizes, two of which contained artificial delaminations. A range of steel ball hammers was used as reference impulse sources, the responses of which were compared with wave excitation generated by a 7 ns pulsed 1064 nm Nd:YAG laser with 150 mJ pulse energy. Signals were recorded by surface-mounted accelerometers and two contactless methods: microphones and a laser Doppler vibrometer. The laser generates frequencies across a broad range of frequencies (0 to 150 kHz) but with much less energy than the hammers' narrower frequency spectra; the laser pulse energy transferred into the specimen is 0.07 mJ, corresponding to about 0.5 ‰ of the impulse source energy. Because of this, the thick intact plates' characteristic thickness stretch resonance frequency can be reliably detected by the hammer excitations but not when using laser excitation. However, the laser can excite low-frequency flexural vibration modes over a shallow delamination at 3 cm depth. The low-frequency flexural vibration results are verified by numerical natural frequency analysis.
Laser powder bed fusion is one of the most promising additive manufacturing techniques for printing complex-shaped metal components. However, the formation of subsurface porosity poses a significant risk to the service lifetime of the printed parts. In-situ monitoring offers the possibility to detect porosity already during manufacturing. Thereby, process feedback control or a manual process interruption to cut financial losses is enabled.
Short-wave infrared thermography can monitor the thermal history of manufactured parts which is closely connected to the probability of porosity formation. Artificial intelligence methods are increasingly used for porosity prediction from the obtained large amounts of complex monitoring data. In this study, we aim to identify the potential and the challenges of deep-learning-assisted porosity prediction based on thermographic in-situ monitoring.
Therefore, the porosity prediction task is studied in detail using an exemplary dataset from the manufacturing of two Haynes282 cuboid components. Our trained 1D convolutional neural network model shows high performance (R2 score of 0.90) for the prediction of local porosity in discrete sub-volumes with dimensions of (700 x 700 x 40) μm³.
It could be demonstrated that the regressor correctly predicts layer-wise porosity changes but presumably has limited capability to predict differences in local porosity. Furthermore, there is a need to study the significance of the used thermogram feature inputs to streamline the model and to adjust the monitoring hardware. Moreover, we identified multiple sources of data uncertainty resulting from the in-situ monitoring setup, the registration with the ground truth X-ray-computed tomography data and the used pre-processing workflow that might influence the model’s performance detrimentally.
Laser powder bed fusion is one of the most promising additive manufacturing techniques for printing complex-shaped metal components. However, the formation of subsurface porosity poses a significant risk to the service lifetime of the printed parts. In-situ monitoring offers the possibility to detect porosity already during manufacturing. Thereby, process feedback control or a manual process interruption to cut financial losses is enabled.
Short-wave infrared thermography can monitor the thermal history of manufactured parts which is closely connected to the probability of porosity formation. Artificial intelligence methods are increasingly used for porosity prediction from the obtained large amounts of complex monitoring data. In this study, we aim to identify the potential and the challenges of deep-learning-assisted porosity prediction based on thermographic in-situ monitoring.
Therefore, the porosity prediction task is studied in detail using an exemplary dataset from the manufacturing of two Haynes282 cuboid components. Our trained 1D convolutional neural network model shows high performance (R² score of 0.90) for the prediction of local porosity in discrete sub-volumes with dimensions of (700 x 700 x 40) μm³.
It could be demonstrated that the regressor correctly predicts layer-wise porosity changes but presumably has limited capability to predict differences in local porosity. Furthermore, there is a need to study the significance of the used thermogram feature inputs to streamline the model and to adjust the monitoring hardware. Moreover, we identified multiple sources of data uncertainty resulting from the in-situ monitoring setup, the registration with the ground truth X-ray-computed tomography data and the used pre-processing workflow that might influence the model’s performance detrimentally.