Machine Learning for Camera-Based Monitoring of Laser Welding Processes

Der zunehmende Einsatz automatisierter Laserschweißprozesse stellt hohe Anforderungen an die Prozessüberwachung. Ziel ist es, eine hohe Fügequalität und eine frühestmögliche Fehlererkennung zu gewährleisten. Durch die Verwendung von Methoden des maschinellen Lernens können kostengünstigere und im Optimalfall bereits vorhandene Sensoren zur Überwachung des gesamten Prozesses eingesetzt werden. In dieser Arbeit werden Methoden aufgezeigt, die mit einer an der Fokussieroptik koaxial zum Laserstrahl integrierten Kamera eine Prozessüberwachung vor, während und nach dem Schweißprozess vornehmen. Zur Veranschaulichung der Methoden wird der Kontaktierungsprozess von Kupferdrähten zur Herstellung von Formspulenwicklungen verwendet. Die vorherige Prozessüberwachung umfasst eine durch ein faltendes neuronales Netz optimierte Bauteillagedetektion. Durch ei ne Formprüfung der detektierten Fügekomponenten können zudem vorverarbeitende Schritte überwacht und die Schweißung fehlerhafter Bauteile vermieden werden. Die prozessbegleitende Überwachung konzentriert sich auf die Erkennung von Spritzern, da diese als Indikator für einen instabilen Prozess dienen. Algorithmen des maschinellen Lernens führen eine semantische Segmentierung durch, die eine klare Unterscheidung zwischen Rauch, Prozesslicht und Materialauswurf ermöglicht. Die Qualitätsbewertung nach dem Prozess beinhaltet die Extraktion von Informationen über Größe und Form der Anbindungsfläche aus dem Kamerabild. Zudem wird ein Verfahren vorgeschlagen, welches anhand eines Kamerabildes mit Methoden des maschinellen Lernens die Höhendaten berechnet. Anhand der Höhenkarte wird eine regelbasierte Qualitätsbewertung der Schweißnähte durchgeführt. Bei allen Algorithmen wird die Integrierbarkeit in industrielle Prozesse berücksichtigt. Hierzu zählen unter anderem eine geringe Datengrundlage, eine begrenzte Inferenzhardware aus der industriellen Fertigung und die Akzeptanz beim Anwender

Quality control of laser welds based on the weld surface and the weld profile

2D or 3D sensor technology can be used for data acquisition to monitor the weld quality during laser welding. Compared to a 2D camera image, the 3D height data contains additional relevant information for quality inspection. However, the disadvantages are system complexity, higher costs, and longer acquisition times. Therefore, we compare two image-based methods with the quality assessment based on height data. The first method uses feature vectors of coaxial acquired grayscale images. The significant advantage is that a camera is often integrated into the laser system, so no additional hardware is required. In the second approach, we use an AI-based single-view 3D reconstruction method. The height profile is calculated from a camera image and used for further quality assessment. Thus, we combine the advantages of 2D data acquisition with higher accuracy in evaluating 3D data. In this paper, we analyze a dataset of welded hairpins with different defect types and compare the quality assessment using the height data acquired with OCT, the feature vectors from the camera images, and the reconstructed height data

Analysis of AI-Based Single-View 3D Reconstruction Methods for an Industrial Application

Machine learning (ML) is a key technology in smart manufacturing as it provides insights into complex processes without requiring deep domain expertise. This work deals with deep learning algorithms to determine a 3D reconstruction from a single 2D grayscale image. The potential of 3D reconstruction can be used for quality control because the height values contain relevant information that is not visible in 2D data. Instead of 3D scans, estimated depth maps based on a 2D input image can be used with the advantage of a simple setup and a short recording time. Determining a 3D reconstruction from a single input image is a difficult task for which many algorithms and methods have been proposed in the past decades. In this work, three deep learning methods, namely stacked autoencoder (SAE), generative adversarial networks (GANs) and U-Nets are investigated, evaluated and compared for 3D reconstruction from a 2D grayscale image of laser-welded components. In this work, different variants of GANs are tested, with the conclusion that Wasserstein GANs (WGANs) are the most robust approach among them. To the best of our knowledge, the present paper considers for the first time the U-Net, which achieves outstanding results in semantic segmentation, in the context of 3D reconstruction tasks. Unlike the U-Net, which uses standard convolutions, the stacked dilated U-Net (SDU-Net) applies stacked dilated convolutions. Of all the 3D reconstruction approaches considered in this work, the SDU-Net shows the best performance, not only in terms of evaluation metrics but also in terms of computation time. Due to the comparably small number of trainable parameters and the suitability of the architecture for strong data augmentation, a robust model can be generated with only a few training data

Tracking 21st century anthropogenic and natural carbon fluxes through model-data integration

Monitoring the implementation of emission commitments under the Paris agreement relies on accurate estimates of terrestrial carbon fluxes. Here, we assimilate a 21st century observation-based time series of woody vegetation carbon densities into a bookkeeping model (BKM). This approach allows us to disentangle the observation-based carbon fluxes by terrestrial woody vegetation into anthropogenic and environmental contributions. Estimated emissions (from land-use and land cover changes) between 2000 and 2019 amount to 1.4 PgC yr −1 , reducing the difference to other carbon cycle model estimates by up to 88% compared to previous estimates with the BKM (without the data assimilation). Our estimates suggest that the global woody vegetation carbon sink due to environmental processes (1.5 PgC yr −1 ) is weaker and more susceptible to interannual variations and extreme events than estimated by state-of-the-art process-based carbon cycle models. These findings highlight the need to advance model-data integration to improve estimates of the terrestrial carbon cycle under the Global Stocktake

Camera-based spatter detection in laser welding with a deep learning approach

Laser welding, semantic segmentation, u-net, quality assurance, spatter detectio

Inclusion and Digitization at School: Conditions for success from the perspective of teachers and students

Inklusion und Digitalisierung stellen zwei grosse Herausforderungen dar, mit denen sich das deutsche Bildungssystem und die einzelnen schulischen Akteure bereits seit Jahren auseinandersetzen müssen. Einzeln betrachtet handelt es sich bei Beiden um intensiv und differenziert beforschte Themenkomplexe. Die Verzahnung beider Konzepte rückt jedoch erst allmählich in den Fokus der Forschung, obwohl sich Berührungspunkte sowie Chancen identifizieren lassen. Der Beitrag gibt einführend eine kurze Übersicht über die aktuellen Rahmenbedingen und Forschungsergebnisse zu den Themenkomplexen Inklusion und Digitalisierung in deutschen Schulen. Im Anschluss daran werden exemplarisch potentielle Voraussetzungen und Bedingungen für die Implementierung der beiden Konzepte zusammengetragen. Besondere Beachtung bei der Darstellung der ausgewählten Bedingungen erhalten die Perspektiven der Lehrkräfte sowie der Schülerinnen und Schüler.Inclusion and digitization are two major challenges that the German education system and the individual school stakeholders have been facing for years. Considered individually, both of these concepts are subject to intensive and differentiated research. However, the fusion of both concepts is only gradually moving into the focus of research, although overlapping points and opportunities can be identified. The following article will briefly outline the current framework conditions and research findings considering inclusion and digitization in schools. Afterwards, the potential requirements and conditions for a successful implementation of the two concepts are summarized. Special attention is given to the perspective of teachers and students

Reliability of Single-Use PEEP-Valves Attached to Self-Inflating Bags during Manual Ventilation of Neonates – An In Vitro Study

Introduction International resuscitation guidelines suggest to use positive end-expiratory pressure (PEEP) during manual ventilation of neonates. Aim of our study was to test the reliability of self-inflating bags (SIB) with single-use PEEP valves regarding PEEP delivery and the effect of different peak inflation pressures (PIP) and ventilation rates (VR) on the delivered PEEP. Methods Ten new single-use PEEP valves from 5 manufacturers were tested by ventilating an intubated 1kg neonatal manikin containing a lung model with a SIB that was actuated by an electromechanical plunger device. Standard settings: PIP 20cmH2O, VR 60/min, flow 8L/min. PEEP settings of 5 and 10cmH2O were studied. A second test was conducted with settings of PIP 40cmH2O and VR 40/min. The delivered PEEP was measured by a respiratory function monitor (CO2SMO+). Results Valves from one manufacturer delivered no relevant PEEP and were excluded. The remaining valves showed a continuous decay of the delivered pressure during expiration. The median (25th and 75th percentile) delivered PEEP with standard settings was 3.4(2.7–3.8)cmH2O when set to 5cmH2O and 6.1(4.9–7.1)cmH2O when set to 10cmH2O. Increasing the PIP from 20 to 40 cmH2O led to a median (25th and 75th percentile) decrease in PEEP to 2.3(1.8–2.7)cmH2O and 4.3(3.2–4.8)cmH2O; changing VR from 60 to 40/min led to a PEEP decrease to 2.8(2.1–3.3)cmH2O and 5.0(3.5–6.2)cmH2O for both PEEP settings. Conclusion Single-use PEEP valves do not reliably deliver the set PEEP. PIP and VR have an effect on the delivered PEEP. Operators should be aware of these limitations when manually ventilating neonates

Responses of Ileal and Fecal Microbiota to Withdrawal of Pancreatic Enzyme Replacement Therapy in a Porcine Model of Exocrine Pancreatic Insufficiency

Little is known regarding the interplay between microbiota and pancreas functions in humans as investigations are usually limited to distal sites, namely the analyses of fecal samples. The aim of this study was to investigate both ileal and fecal microbiota in response to pancreatic enzyme replacement therapy (PERT) in a porcine model of exocrine pancreatic insufficiency (EPI). PERT was stopped for ten days in ileo-cecal fistulated minipigs with experimentally induced EPI (n = 8) and ileal digesta as well as fecal samples were obtained before withdrawal, during withdrawal and after the reintroduction of PERT. Profound community changes occurred three days after enzyme omission and were maintained throughout the withdrawal phase. A reduction in α-diversity together with relative abundance changes in several taxa, in particular increases in Bifidobacteria (at both sites) and Lactobacilli (only feces) were observed. Overall, dysbiosis events from the ileum had accumulating effects in distal parts of the gastrointestinal tract with additional alterations occurring only in the colon. Changes were reversible after continuing PERT, and one week later, bacterial communities resembled those at baseline. Our study demonstrates the rapid and profound impacts of enzyme withdrawal in bacterial communities, contributing to our understanding of the interplay between pancreas function and microbiot

Comparison of uncertainties in land-use change fluxes from bookkeeping model parameterisation

Fluxes from deforestation, changes in land cover, land use and management practices (FLUC for simplicity) contributed to approximately 14 % of anthropogenic CO2 emissions in 2009–2018. Estimating FLUC accurately in space and in time remains, however, challenging, due to multiple sources of uncertainty in the calculation of these fluxes. This uncertainty, in turn, is propagated to global and regional carbon budget estimates, hindering the compilation of a consistent carbon budget and preventing us from constraining other terms, such as the natural land sink