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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.
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.
The manufacturing of metal parts for the use in safety-relevant applications by Laser Powder Bed Fusion (L-PBF) demands a quality assurance of both part and process. Thermography is a nondestructive testing method that allows the in-situ determination of the thermal history of the produced part which is connected to the mechanical properties and the formation of defects [1]. A wide range of commercial thermographic camera systems working in different spectral ranges is available on the market. The understanding of the applicability of these cameras for qualitative and quantitative in-situ measurements in L-PBF is of vital importance [2]. In this study, the building process of a cylindrical specimen (Inconel 718) is monitored by three camera systems simultaniously. These camera systems are sensitive in various spectral bandwidths providing information in different temperature ranges. The performance of each camera system is explored in the context of the extraction of image features for the detection of defects. It is shown that the high temporal and thermal process dynamics are limiting factors on this matter. The combination of different spectral camera systems promises the potential of an improved defect detection by data fusion.
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.
The detection of delamination, rot and cracks causing a decrease of strength in wooden construction elements is a key task for nondestructive testing (NDT). Air-coupled ultrasound (ACU) is used to detect flaws without having to provide contact to the surface or otherwise affect the object. Novel ferroelectric transducers with a high signal-to-noise ratio enable an accurate flaw detection. Transducers made of cellular polypropylene (PP) are suitable for characterizing wood-based materials (WBM) because their extremely low Young’s modulus and low density mean a favorable acoustic impedance for the transmission of ultrasonic waves between the transducer and air. The transducers can be applied during the production of WBM and during its service life. The device enables a fast in-situ recognition of defects with frequencies between 90 kHz and 250 kHz. Measurements can be performed in through transmission for a high resolution or in pulse-echo technique in case of one-sided access at the expense of reduced signal strength. 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 detection of delamination, rot and cracks causing a decrease of strength in wooden construction elements is a key task for nondestructive testing (NDT). Air-coupled ultrasound (ACU) is used to detect flaws without having to provide contact to the surface or otherwise affect the object. Novel ferroelectric transducers with a high signal-to-noise ratio enable an accurate flaw detection. Transducers made of cellular polypropylene (PP) are suitable for characterizing wood-based materials (WBM) because their extremely low Young’s modulus and low density mean a favorable acoustic impedance for the transmission of ultrasonic waves between the transducer and air. The transducers can be applied during the production of WBM and during its service life. The device enables a fast in-situ recognition of defects with frequencies between 90 kHz and 250 kHz. Measurements can be performed in through transmission for a high resolution or in pulse-echo technique in case of one-sided access at the expense of reduced signal strength. 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 detection of delamination, rot, and cracks in wooden construction elements is a key task for nondestructive testing (NDT). 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. After interpretation of the inspection data, an assessment of the mechanical properties based on an appraisal of internal defects in the material is feasible. 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, structures with great dimensions, thickness up to 300 mm and material densities under 500 kg/m³ can be inspected. Promising results were obtained under laboratory conditions with frequencies from 90 kHz to 200 kHz. The advantage of 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 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.