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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.
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.
The detection of internal irregularities is crucial for quality assessment in metal-based additive manufacturing (AM) technologies such as laser powder bed fusion (L-PBF). The utilization of in-process thermography as an in situ monitoring tool in combination with post-process X-ray micro computed tomography (XCT) as a reference technique has shown great potential for this aim. Due to the small irregularity dimensions, a precise registration of the datasets is necessary as a
requirement for correlation. In this study, the registration of thermography and XCT reference datasets of a cylindric specimen containing keyhole pores is carried out for the development of a porosity prediction model. The considered datasets show variations in shape, data type and dimensionality, especially due to shrinkage and material elevation effects present in the manufactured part. Since the resulting deformations are challenging for registration, a novel preprocessing methodology is introduced that involves an adaptive volume adjustment algorithm which is based on the porosity distribution in the specimen. Thus, the implementation of a simple three-dimensional image-to-image registration is enabled. The results demonstrate the influence of the part deformation on the resulting porosity location and the importance of registration in terms of irregularity prediction.
Additive manufacturing (AM) technologies, generally called 3D printing, are widely used because their use provides a high added value in manufacturing complex-shaped components and objects. Defects may occur within the components at different time of manufacturing, and in this regard, non-destructive techniques (NDT) represent a key tool for the quality control of AM components in many industrial fields, such as aerospace, oil and gas, and power industries. In this work, the capability of active thermography and eddy current techniques to detect real imposed defects that are representative of the laser powder bed fusion process has been investigated. A 3D complex shape of defects was revealed by a μCT investigation used as reference results for the other NDT methods. The study was focused on two different types of defects: porosities generated in keyhole mode as well as in lack of fusion mode. Different thermographic and eddy current measurements were carried out on AM samples, providing the capability to detect volumetric irregularly shaped defects using non-destructive methods.
This paper investigates fast and inexpensive measurement methods for defect detection in parts produced by Additive Manufacturing (AM) with special focus on lattice parts made of ceramics. By Lithography-based Ceramic Manufacturing, parts were built both without defects and with typical defects intentionally introduced. These defects were investigated and confirmed by industrial X-ray Computed Tomography. Alternative inexpensive methods were applied afterwards on the parts such as weighing, volume determination by Archimedes method and gas permeability measurement. The results showed, that defects resulting in around 20% of change in volume and mass could be separated from parts free of defects by determination of mass or volume. Minor defects were not detectable as they were in the range of process-related fluctuations. Permeability measurement did not allow to safely identify parts with defects. The measurement methods investigated can be easily integrated in AM process chains to support quality control.
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 capabilities of non-destructive testing (NDT) methods for defect detection in civil engineering are characterized by their different penetration depth, resolution and sensitivity to material properties. Therefore, in many cases multi-sensor NDT has to be performed, producing large data sets that require an efficient data evaluation framework. In this work an image fusion methodology is proposed based on unsupervised clustering methods. Their performance is evaluated on ground penetrating radar and infrared thermography data from laboratory concrete specimens with different simulated near-surface defects. It is shown that clustering could effectively partition the data for further feature level-based data fusion by improving the detectability of defects simulating delamination, voids and localized water. A comparison with supervised symbol level fusion shows that clustering-based fusion outperforms this, especially in situations with very limited knowledge about the material properties and depths of the defects. Additionally, clustering is successfully applied in a case study where a multi-sensor NDT data set was automatically collected by a self-navigating mobile robot system.