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First-time-right printing is needed for extensive industrialization of Wire Arc Additive Manufacturing. However, due to process instabilities defects can occur even if suitable process parameter were chosen, resulting in production scrap due to an insufficient part quality. In this paper, we propose a smart manufacturing system which enables the compensation of previously created defects by means of a Digital Twin. We predict the future position of the welding torch, analyze its spatial context and adapt the process parameter if needed accordingly to compensate defects. Using this approach, a fault-tolerant manufacturing process is enabled, resulting in a first-time-right process.

Wire Arc Additive Manufacturing allows the cost-effective manufacturing of customized, large-scale metal parts. As the post-process quality assurance of large parts is costly and time-consuming, process monitoring is inevitable. In the present study, a context-aware monitoring solution was investigated by integrating machine, temporal, and spatial context in the data analysis. By analyzing the voltage patterns of each cycle in the oscillating cold metal transfer process with a deep neural network, temporal context was included. Spatial context awareness was enabled by building a digital twin of the manufactured part using an Octree as spatial indexing data structure. By means of the spatial context awareness, two quality metrics—the defect expansion and the local anomaly density—were introduced. The defect expansion was tracked in-process by assigning detected defects to the same defect cluster in case of spatial correlation. The local anomaly density was derived by defining a spherical region of interest which enabled the detection of aggregations of anomalies. By means of the context aware monitoring system, defects were detected in-process with a higher sensitivity as common defect detectors for welding applications, showing less false-positives and false-negatives. A quantitative evaluation of defect expansion and densities of various defect types such as pore nests was enabled.

Odontogenic sinusitis (ODS) can cause infectious orbital, intracranial, and osseous complications. Diagnosis and management of complicated ODS have not been discussed in recent sinusitis guidelines. The purpose of this systematic review was to describe epidemiological and clinical features, as well as management strategies of complicated ODS.PubMed, EMBASE, and Cochrane Library.A systematic review was performed to describe various features of complicated ODS. All complicated ODS studies were included in qualitative analysis, but studies were only included in quantitative analysis if they reported specific patient-level data.Of 1126 studies identified, 75 studies with 110 complicated ODS cases were included in qualitative analysis, and 47 studies with 62 orbital and intracranial complications were included in quantitative analyses. About 70% of complicated ODS cases were orbital complications. Only 23% of complicated ODS studies were published in otolaryngology journals. Regarding ODS-related orbital and intracranial complications, about 80% occurred in adults, and 75% were male. Complicated ODS occurred most commonly from apical periodontitis of maxillary molars. There were no relationships between sinusitis extent and orbital or intracranial complications. High rates of anaerobic and α-hemolytic streptococcal bacteria were identified in complicated ODS. Management generally included systemic antibiotics covering aerobic and anaerobic bacteria, and surgical interventions were generally performed to address both the complications (orbital and/or intracranial) and possible infectious sources (dentition and sinuses).ODS should be considered in all patients with infectious extrasinus complications. Multidisciplinary management between otolaryngologists, dental specialists, ophthalmologists, and neurosurgeons should be considered to optimize outcomes.

In recent years, the interest in the improved functionalisation of Additive Manufacturing components through multi-material solutions has increased because of the new possibilities in product design. In this work, an advanced Wire Arc Additive Manufacturing process for fabrication of multi-material structures of different aluminium alloys was investigated. Mechanical properties such as tensile strength, yield strength, fracture elongation, and hardness were analysed for multi-material parts and compared with the mechanical properties of mono-material parts. It was found that the strength of multi-material components was limited by the properties of the individual aluminium alloys and not by those of the material transition zones. Microsections and EDX line scans revealed a smooth transition zone without any significant defects. Furthermore, process monitoring approaches for quality assurance of the correct material composition in such multi-material structures were investigated. Different sensor data were captured during multi-material Wire Arc Additive Manufacturing to identify and observe various characteristics of the process. It was shown that the voltage, current, acoustic, and spectral emission data can be used for in-situ monitoring to detect the chemical differences between the two aluminium alloys 6060 and 5087. Characteristic patterns in the frequency range were found, which can be attributed to a frequency shift that occurred due to the different material properties. Spectral analysis revealed changes in the ratios of green and blue light emission to red light emission, which was also due to the different magnesium contents.

Wire Arc Additive Manufacturing (WAAM) offers the possibility to build up large-scale metal parts. Data which is obtained from a multivariate sensor system in-situ must be analyzed automatically to ensure an early and reliable detection of defects to reduce the costs due to production scrap. For that reason, a modular anomaly detector for multivariate time series in WAAM was investigated in this paper. The approach adressed major topics in real-life data sets of industrial applications such as miscellaneous signal sample rates, lack of synchronization and concept drift. A reference data set based on an anomaly-dependently splitted time horizon was defined to reduce the sensitivity loss of the detector after an anomaly. To avoid the need for labeled data, an unsupervised anomaly detection method based on neural networks was used. Hence, no time and costs for artificial defect creation on the machine tool are required when implementing the approach in industrial applications.

Wire Arc Additive Manufacturing is a near-net-shape machining technology that enables low-cost production of large and customised metal parts. In the present work, oxidation effects in Wire Arc Additive Manufacturing of the aluminium alloy AW4043/AlSi5(wt%) were investigated. Two main oxidation effects, the surface oxidation on aluminium parts and the oxidation anomalies in aluminium parts were observed and analysed. The surface oxidation on aluminium parts changed its colour during Wire Arc Additive Manufacturing from transparent to white. In the present work, it was shown by high-speed imaging that this change in the surface oxidation took place in the process zone, which was covered by inert gas. Since the white surface oxidation formed in an inert gas atmosphere, it was found that the arc interacts with the existing amorphous oxide layer of the previously deposited layer and turns it into a white duplex (crystalline and amorphous) oxide layer. In addition to the analysis of the white surface oxidation, oxidation anomalies, which occur at low shielding from the environment, were investigated. It was shown by physical experiments and Computational Fluid Dynamics simulations, that these oxidation anomalies occur at inadequate gas flow rates, too big nozzle-to-work distances, process modes with too high heat input, or too high wire feed rates. Finally, a monitoring method based on light emission spectroscopy was used to detect oxidation anomalies as they create peaks in the spectral emission when they occur.

Wire Arc Additive Manufacturing is a near-net-shape processing technology which allows cost-effective manufacturing of large and customized metal parts. Processing of aluminium in Wire Arc Additive Manufacturing is quite challenging, especially in terms of porosity. In the present work, pore behaviour in Wire Arc Additive Manufacturing of AW4043/AlSi5(wt%) was investigated and a post-process monitoring approach was developed. It has been observed that as the shielding gas flow rate increases, the porosity in aluminium parts also increases due to the rapid solidification of the melt pool by forced convection. The higher convection rate seems to limit the escape of gas inclusions. Furthermore, gas inclusions escaping from the melt pool leave cavities on the surface of each deposited layer. Process camera imaging is used to monitor these cavities to acquire information about the porosity in the part. The observations were supported by Computational Fluid Dynamics simulations which show that the gas flow rate correlates with the porosity in aluminium parts manufactured by Wire Arc Additive Manufacturing. Since a lower gas flow rate leads to reduced convective cooling, the melt pool remains liquid for a longer period allowing pores to escape for a longer period and thus reducing porosity. Based on these investigations, a monitoring approach is presented.

Robot-based wire arc additive manufacturing processes enable the cost-effective production of large-volume metallic components. With the aid of a monitoring system, it is possible to react adaptively to process instabilities.

Tool wear is one of the main factors of manufacturing costs in subtractive manufacturing processes. To control manufacturing processes while taking the tool wear into account, a variety of tool condition monitoring systems have been investigated. In this paper, we present a new approach to support the manual analysis of tool wear images by the means of semantic image segmentation. We utilize deep learning for image evaluation through semantic classification of different defect regions. In this study, a small-sized dataset of 100 cutting tool inserts at different tool conditions, exhibiting various wear defects, is acquired and masked by a process expert. A sliding window approach is used to extract small size feature maps from the raw images, with the class of the center pixel as the label. The relationship between the features and the label is trained using a convolutional neural network. Our investigation shows that this network can predict the wear defect class of each pixel with an accuracy of over 91%. Compared to other approaches, the proposed solution can differentiate between various defect types, for instance, flank wear, groove formation and build-up-edge. From the resulting segmented image, different wear metrics are computed, such as the maximum flank wear width or the occurrence and size of other wear defects. This information is fed back to the machine operator to support the decision process of whether to continue machining, adapt the cutting conditions or exchange the insert.

In condition monitoring of cutting inserts for machine tools, vision-based solutions enable detailed wear pattern analysis. Besides the main failure modes of flank wear and tool breakage, other defects, such as chipping, grooves, and build-up-edges, can be detected and quantified. However, manual analysis of the images is time consuming and traditional machine vision systems have limited capabilities adapting to changing situations, such as different insert types. As a result, robust machine learning techniques are researched to support the process of classifying images and detecting defects through image segmentation. For the latter, a variety of highly optimized networks exists. Still, these networks require tuning by machine learning experts. In contrast, automated machine learning is a recent trend that greatly reduces these efforts through automated network selection and hyperparameter optimization. In this study, automated machine learning is compared with manually trained segmentation networks on the example of tool condition monitoring. To achieve this, a heterogeneous dataset of over 200 industrial cutting tool images is recorded and evaluated. Comparing the manually trained segmentation networks to the automated machine learning framework, it is determined that the automated machine learning solution is easier to handle, faster to train and achieves better accuracies than other approaches.

Wire Arc Additive Manufacturing is a near-net-shape processing technology which allows the cost-effective manufacturing of big and customized metal parts. In the present work the Wire Arc Additive Manufacturing of AW4043/AlSi5(wt.%) with different lead angles of the welding torch was investigated. It has been shown that for some lead angles fluctuation effects occur in the structures produced if the interlayer temperature is either too low or too high. All experiments were analysed by high-speed imaging whereby the welding phenomena could be observed. In the case of Wire Arc Additive Manufacturing with a lead angle above 10° at lower interlayer temperatures, the deposited track consists out of several, seperated WAAM globules and is no longer in a uniform track. In the case of the dragging and neutral Wire Arc Additive Manufacturing processes at higher interlayer temperatures, fluctuation effects occur. In addition, by evaluating the high-speed videos with computer vision, it was found that such fluctuation effects can be detected at the arc frequency of the process. To avoid fluctuation effects caused by too low or too high interlayer temperatures, a pushing Wire Arc Additive Manufacturing process with a slightly tilted lead angle should be used.

Malignant peripheral nerve sheath tumors (MPNSTs) are a rare and aggressive subtype of sarcomas defined by their neural origin. Head and neck manifestations are particularly uncommon. Challenges exist in diagnosis, management, and recurrence. Achieving local control, particularly in the head and neck region, is difficult. We present a patient with a rapidly enlarging MPNST on the right parietal calvarium shortly after resection of a right vagus nerve MPNST. Recommendation was made for excision and reconstruction with a local advancement flap followed by radiation therapy. Local control with good aesthetic outcome was achieved by applying recommended surgical and oncologic principles.

Fragestellung: Zur Versorgung der Humeruskopftrümmerfraktur stehen die osteosynthetische Versorgung mittels winkelstabiler Platte oder die endoprothetische Versorgung zur Verfügung. Derzeit existiert nur eine geringe Datenlage darüber, welches dieser Verfahren bei komplexen Frakturen [for full text, please go to the a.m. URL], Deutscher Kongress für Orthopädie und Unfallchirurgie (DKOU 2014)

Expression studies in the yeast S. pombe have been utilised to establish the basis for a genetic analysis designed to identify the lethal partners of the pro-apoptotic proteins bak and bax. Bak expression in S. pombe is lethal and this lethality is rescued by co-expression of bcl-2 or bcl-x(L). S. pombe cells expressing bak have a terminal phenotype in which the majority of cells are blocked in the G1 phase of the cell cycle while the remainder of cells, unable to complete M-phase, mis-coordinate the timing of subsequent events in the cell cycle. Although bax expression in S. pombe gives rise to a slow growth phenotype, not a lethality, bax expressing cells display the same cell cycle phenotypes described for bak. Electron microscopy of cells expressing bak reveals a dramatic accumulation of large vesicular structures. A two-hybrid screen designed to identify S. pombe proteins which interact with bak, isolated the S. pombe calnexin homologue cnx1. Genetic analysis demonstrates that the Cnx1 domain which binds to bak in two-hybrid experiments, is necessary for bak lethality in S. pombe. This report identifies a lethal interacting partner for bak and the observations suggest a model for bak mediated lethality which can be tested in higher cells.

Fragestellung: Bei proximalen Humerusschaftfrakturen stehen als operative Standardverfahren sowohl die geschlossene Reposition mit intramedullärer Nagelung, als auch die offene Reposition mit Osteosynthese mittels winkelstabiler langer Formplatte zur Verfügung. Vorteile beider Verfahren könnten[for full text, please go to the a.m. URL], Deutscher Kongress für Orthopädie und Unfallchirurgie (DKOU 2013)