FSP3: Produktion
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The increasing use of simulation technologies, especially virtual commissioning, in the context of modern plant development for manufacturing discrete parts is driven by the pressure to shorten time-to-market cycles and overcome supply bottlenecks. The need for robust technologies to seamlessly integrate the digital and physical world is growing as machine data becomes more readily available. A challenge to this integration is presented by the need to continuously adjust the movement parameters, especially for event-discrete actuators based on live data, taking wear, ageing and process-time fluctuations into account. A lack of synchronization leads to discrepancies between the simulation and reality renders them useless. Related works in this field are discussed, which highlight the complexities of achieving synchronization between simulation and reality, particularly in event-discrete signals and systems. The aim of this article is to present a method for reusing virtual commissioning models for operation-synchronized simulations at actuator level. This approach includes introducing of a methodology called prescheduling in order to compensate process uncertainties and also defines the necessary requirements for the simulation tool and model. The method is validated using an industrial test system and a commercial virtual commissioning tool to confirm ist suitability for real-life implementation in industrial plants, which suggests its suitability for improving production efficiency and reducing costs by means of machine monitoring and proactive control interventions.
Using a Machine Learning Regression Approach to Predict the Aroma Partitioning in Dairy Matrices
(2024)
Aroma partitioning in food is a challenging area of research due to the contribution of several physical and chemical factors that affect the binding and release of aroma in food matrices. The partition coefficient measured by the Kmg value refers to the partition coefficient that describes how aroma compounds distribute themselves between matrices and a gas phase, such as between different components of a food matrix and air. This study introduces a regression approach to predict the Kmg value of aroma compounds of a wide range of physicochemical properties in dairy matrices representing products of different compositions and/or processing. The approach consists of data cleaning, grouping based on the temperature of Kmg analysis, pre-processing (log transformation and normalization), and, finally, the development and evaluation of prediction models with regression methods. We compared regression analysis with linear regression (LR) to five machine-learning-based regression algorithms: Random Forest Regressor (RFR), Gradient Boosting Regression (GBR), Extreme Gradient Boosting (XGBoost, XGB), Support Vector Regression (SVR), and Artificial Neural Network Regression (NNR). Explainable AI (XAI) was used to calculate feature importance and therefore identify the features that mainly contribute to the prediction. The top three features that were identified are log P, specific gravity, and molecular weight. For the prediction of the Kmg in dairy matrices, R2 scores of up to 0.99 were reached. For 37.0 °C, which resembles the temperature of the mouth, RFR delivered the best results, and, at lower temperatures of 7.0 °C, typical for a household fridge, XGB performed best. The results from the models work as a proof of concept and show the applicability of a data-driven approach with machine learning to predict the Kmg value of aroma compounds in different dairy matrices.
Effiziente Kühlung und Schmierung für Fräsprozesse: Frästechnologie und Hochdruckkühlschmierstoff
(2024)
Insbesondere bei der 5-achsigen Fräsbearbeitung existiert noch kein System, das die optimale bzw. minimale Hochdruckkühlschmierstoffmenge bei jeweils gegebenen Bearbeitungszuständen beschreibt.
Die Entwicklung, Umsetzung und Potentiale eines seriennah einsetzbaren, externen und vom Werkzeugmaschinenhersteller unabhängigen Systems zur optimierten Bereitstellung von Hochdruckkühlschmierstoff werden gezeigt.
Bisher existiert noch kein System, das die optimale bzw. minimale Kühlschmierstoffmenge bei jeweils gegebenen Bearbeitungssituationen unter besonderer Berücksichtigung der zu verarbeitenden Werkstoffe und deren signifikantem Verschleißverhalten beschreibt.
Die Entwicklung eines seriennahen unabhängigen Systems zur optimierten Bereitstellung von Kühlschmierstoffen mit angepassten Drücken für die jeweilige Werkstoffapplikationen wird gezeigt.
Diese Veröffentlichung beleuchtet die Herausforderungen und Lösungsansätze zur Bewältigung des Fachkräftemangels in der deutschen produzierenden Industrie. Der Mangel an qualifizierten Arbeitskräften verursacht erhebliche Kosten und verringert das Produktionspotenzial. Hauptursachen sind demografische Veränderungen und veränderte Wertvorstellungen der Beschäftigten. Die Attraktivität des Arbeitsumfelds soll durch die Sichtbarmachung individueller Beiträge und die Stärkung des Gemeinschaftsgefühls erhöht werden. Maßnahmen aus dem Umfeld von Large-Language-Models werden vorgestellt. Abschließend wird die Vision einer modernen, vollvernetzten und arbeitnehmerfreundlichen Produktionsstätte skizziert.
In an industrial context, AI-based methods are becoming increasingly important in the optical systems used for identification, inspection and classification. The reasons for this are that AI-based image processing algorithms are easy to use on the operator side and often achieve superior results. E.g. in complex classification tasks. In the sand cast industry, the complexity in optical inspection of cast parts is connected with strong variations in the local surface topography and in the global object geometry change. Despite the great potential of AI-based methods, application is often hindered by the immense effort involved in acquiring a suitable training dataset. This refers not only to the acquisition of the required number of images but also to the tedious labelling. In this work, we investigate the capabilities and limits of synthetic training data on an AI-based optical scanner used to identify and track cast parts. The optical scanner is capable of detecting and classifying a codification specifically designed for the casting industry. By reading the code, the scanner can deduce the specific number of the cast part. For synthetic image generation, we use physically based rendering, which has advantage of full control over all rendering parameters. This allows for both a systematic investigation of the importance of the parameters and, an automatic labelling process of the training datasets. Our results show that, in particular, a detailed geometric modelling of the local surface topography and global object geometry of the pins have a positive influence on the recognition rate of the neural network. With that accuracy rates up to 56 % are achieved using synthetic training datasets, only.
Anomalie-Detektion im Kontext der Industrie 4.0 hat gerade seit der wachsenden Popularität von maschinellen Lernverfahren und aufkommenden großen Datenmengen an Relevanz gewonnen. So kann in Herstellungsprozessen über die Zeit unerwartetes Verhalten auftreten, das für den Ausschuss von Teilen verantwortlich ist. Es besteht ein Interesse an den Ursachen dieses Verhaltens, sodass ggf. einem nochmaligen Auftreten proaktiv entgegengewirkt werden kann. Aus den aufgenommenen Daten der Sensoren für den Prozess lassen sich Ursachen für dieses Verhalten ableiten. Es besteht bereits Forschung für die Detektion von Anomalien in Sensordaten, jedoch meist im univariaten Fall, d.h unter Beobachtung einer Zielgröße. Zudem besteht ein Forschungsbedarf bei der Erkennung von Anomalien, die sich nicht punktweise manifestiert, sondern über einer Menge von beobachteten Daten. Dafür verfügbare sind allerdings ebenfalls nur spärlich vorhanden und verfügen über keine Nähe zu aus Sensoren gewonnen Daten. Diese Arbeit widmet sich diesen Herausforderungen, indem sie die Problemstellung ausformuliert, den aktuellen Forschungsstand wiedergibt und mit einem praktischen Teil Lösungsansätze zur Verfügung stellt. So wird eine Komponente zur Generierung von synthetischen hoch-dimensionalen Daten entwickelt, die Prozesskurven nachempfunden sind, und damit über eine Ähnlichkeit zu Sensordaten verfügen. Zudem enthalten die erzeugten Daten Informationen über eingepflegte Anomalien, was eine Evaluation von Algorithmen und Modellen ermöglicht. Es werden Verfahren basierend auf dem Stand der Forschung entwickelt und auf synthetischen Daten aus dieser Datengenerierungskomponente evaluiert. Abschließend wird ein Fazit über die Ergebnisse dieses Benchmarks getroffen und ein Ausblick auf die weitere Forschung in diesem Themenfeld gegeben.
Clubfoot is a common congenital foot deformity that leads to constant pain and significant limitations if left untreated or not treated adequately. The most used method for treating clubfoot is the Ponseti method. It involves a correction phase where about five plaster casts are applied and changed weekly. This treatment lasting about 2 to 3 months, is the most chosen method due to its high success rate. However, treated babies often experience skin complications caused by stiff and tight casts. Previous research showed that viable solutions already exist including orthoses. In this research, a developed method known as VDI 2221 was applied and the printable orthosis using 3D printer was selected as an alternative to Ponseti method. Calculations and finite element method (FEM) analysis demonstrated that the orthosis made of PA6-CF provides sufficient stiffness and strength, assuming the weight force of the foot is 10 N. The selected design was developed based on requirements and functional analysis, effectively mitigating the disadvantages of the Ponseti method. The developed orthosis can be manufactured globally using the 3D printing process, with a manufacturing cost of around €150, excluding assembly costs. In summary, a new solution was proposed within the same treatment method, effectively eliminating skin complications, and enabling cost-effective manufacturability on a global scale.
Algorithms for causal discovery have recently undergone rapid advances and increasingly draw on flexible nonparametric methods to process complex data. With these advances comes a need for adequate empirical validation of the causal relationships learned by different algorithms. However, for most real and complex data sources true causal relations remain unknown. This issue is further compounded by privacy concerns surrounding the release of suitable high-quality data. To tackle these challenges, we introduce causalAssembly, a semisynthetic data generator designed to facilitate the benchmarking of causal discovery methods. The tool is built using a complex real-world dataset comprised of measurements collected along an assembly line in a manufacturing setting. For these measurements, we establish a partial set of ground truth causal relationships through a detailed study of the physics underlying the processes carried out in the assembly line. The partial ground truth is sufficiently informative to allow for estimation of a full causal graph by mere nonparametric regression. To overcome potential confounding and privacy concerns, we use distributional random forests to estimate and represent conditional distributions implied by the ground truth causal graph. These conditionals are combined into a joint distribution that strictly adheres to a causal model over the observed variables. Sampling from this distribution, causalAssembly generates data that are guaranteed to be Markovian with respect to the ground truth. Using our tool, we showcase how to benchmark several well-known causal discovery algorithms.
Wide bandgap semiconductors, SiC and GaN-based power devices represent key candidates in the development of more efficient devices due to their superior electrical and thermal properties compared to silicon. To achieve maximal performance from WBG semiconductors, new packaging technologies and thermo-electric designs must be developed to ensure efficient and fast switching of devices while minimizing losses. The paper aims to investigate the thermal and mechanical behavior of new prepackage embedding technologies by finite element simulation. The focus is on insulated substrates including direct bonded copper (DBC) with various dielectrics such as AlN, Al 2O 3, Si3N 4 and new insulated metal substrates (IMS) with emphasis on commercially available materials and thicknesses. This study proposes a thermo-mechanical pareto-optimization methodology able to identify the best substrate configuration. The sintered silver layer (in both sides of the chip), which is the most prone to failure due to delamination, has been modelled with a temperature-dependent bilinear hardening model to account for plasticity. Pareto-optimization accounts for the module thermal resistance and the plastic strain or Von Mises Stress in the sintered layer. Results demonstrate that the best candidate from the thermo-mechanical point of view is the DBC with AlN showing a thermal resistance of 0.34 K/W, accumulative plastic strain of 0.18 % and Von Mises stress of 274 MPa. Finally, the parasitic inductance of multiple pre-packages is evaluated to scale the power of the module. Proper design allows to achieve a stray inductance as small as 1.23 nH for two prepackages and 2.85 nH for four prepackages.
Horizontal chip cracks have been reported in various scientific publications on PCB embedded power semiconductor devices. This study investigates in detail the root cause of the cracks. Experimental evidence indicates that the chip fractures in the mechanical grinding process during preparation of the cross-sections. Here, two different factors are relevant: First, the mechanical fracture strength of the semiconductor die decreases when grinding its edge. The use of P320 sand paper reduces the characteristic fracture strength from 719 MPa to 211 MPa. Second, the tensile stresses in the chip edge increase considerably when, part of the die and package is removed by grinding. Both effects together result in a failure probability of 100%. The use of finer grain sandpaper for target preparation helps to reduce the probability of generating horizontal chip cracks during cross-sectioning.
Die Veröffentlichung beleuchtet die Herausforderungen und Lösungsansätze zur Bekämpfung des Fachkräftemangels innerhalb der produzierenden Industrie am Standort Deutschland. Der Mangel an qualifizierten Arbeitskräften führt zu erheblichen Kosten und reduziert das Produktionspotenzial. Hauptursachen sind der demographische Wandel und veränderte Wertvorstellungen der Beschäftigten. Es gillt, das Arbeitsumfeld Produktion attraktiver zu gestalten, indem individuelle Beiträge sichtbarer gemacht und das Gemeinschaftsgefühl gestärkt werden. Zudem soll die wahrgenommene Komplexität reduziert und die Autonomie des Fertigungspersonals erhöht werden. Beispiele wie ein digitales Ampelsystem und die Visualisierung individueller Beiträge verdeutlichen diese Ansätze. Die Zukunft der Produktion wird durch sieben Thesen skizziert, die die Bedeutung einer partizipativen Planung, die Veränderung der Anforderungen und die Notwendigkeit eines Kommunikationsraums betonen. Die Präsentation endet mit der Vision einer modernen, vollvernetzten und arbeitnehmerfreundlichen Produktionsstätte.
Many cities in Europe and around the world are concerned with reducing their CO2-emissions. One step on this agenda is the introduction of electric buses to replace combustion engines. The electrification of urban buses requires an accurate prediction of the energy demand. In this pa per, an energy model and the underlying calibration process is presented. This approach leverages substantial tracking data from 10 electric buses operated in Göttingen, Germany. It was shown that, with the use of additional information from the directly measured tracking data, like auxiliary power, charging power and vehicle weight, it is possible to precisely calibrate models based on physical equations with regard to generally poorly identifiable parameters like rolling friction coefficient or efficiency of the electric machine. With a multilayered approach for simulating the energy demand, it is possible to validate the results on the mechanical layer and on the electrical layer separately. This enables a far better parametrization and elimination of uncertainties from the different parameters. Furthermore, we compare the results to other publications for sections with 1 km, 100 km and 230 km, respectively. The relative errors between the simulated and measured electrical power consumption are below 0.3%, 3% and 6.5%, respectively. Hence, the yielded model is appropriate for electric urban bus network planning applications. And the found parameters should be a good starting point for other energy prediction models. To further enable comparability with other approaches the dataset used for calibration is made publicly available.
Many cities in Europe and around the world are concerned with reducing their CO2-emissions. One step on this agenda is the introduction of electric buses to replace combustion engines. The electrification of urban buses requires an accurate prediction of the energy demand. In this pa per, an energy model and the underlying calibration process is presented. This approach leverages substantial tracking data from 10 electric buses operated in Göttingen, Germany. It was shown that, with the use of additional information from the directly measured tracking data, like auxiliary power, charging power and vehicle weight, it is possible to precisely calibrate models based on physical equations with regard to generally poorly identifiable parameters like rolling friction coefficient or efficiency of the electric machine. With a multilayered approach for simulating the energy demand, it is possible to validate the results on the mechanical layer and on the electrical layer separately. This enables a far better parametrization and elimination of uncertainties from the different parameters. Furthermore, we compare the results to other publications for sections with 1 km, 100 km and 230 km, respectively. The relative errors between the simulated and measured electrical power consumption are below 0.3%, 3% and 6.5%, respectively. Hence, the yielded model is appropriate for electric urban bus network planning applications. And the found parameters should be a good starting point for other energy prediction models. To further enable comparability with other approaches the dataset used for calibration is made publicly available.
Produkte werden heutzutage immer variantenreicher und individueller. Für die industrielle Fertigung wachsen die Anforderungen, die Produkte effizient zu fertigen und schnell auf sich verändernde Marktbedingungen zu reagieren. Entsprechend rapide steigt die Nachfrage nach flexiblen Produktionslösungen, die sich möglichst autonom an die neuesten Marktanforderungen anpassen. Wandlungsfähige und modulare Produktionssysteme sollen es ermöglichen, auch bei großer Variantenvielfalt effizient zu produzieren - sogar bis zur Losgröße 1. Unterstützt wird die Produktion durch digitale Lösungen, die bereits an vielen Stellen zum Einsatz kommen.
Der Arbeits- und Fachkräftemangel wird seitens der produzierenden Industrie im DACH-Raum als zunehmend wachstumshemmend und geschäftsmodellbedrohend empfunden. Als zentrale Ursachen können gleichermaßen der demographische Wandel wie auch ein entsprechender Wertewandel ausgemacht werden. Es erscheint daher empfehlenswert, die "Ressource Mensch" in dieser Gemengelage neu zu denken. Die Frage, wie ein modernes Produktionssystem beschaffen sein muss, damit dieses aus Arbeitnehmersicht langfristig als attraktiv wahrgenommen wird, rückt hierbei in den Mittelpunkt wissenschaftlicher Überlegungen.
„Hybride Montage“ als Antwort auf Modell Mix und Variantenvielfalt im produzierenden Mittelstand
(2024)
Die "Hybride Montage" als Kombination aus konventioneller Fließfertigung und innovativer Matrixproduktion offeriert vielfältige Potentiale, dem zunehmenden Maß an Variantenvielfalt in der produzierenden Industrie Rechnung zu tragen. Im Zeitalter des Industrial Metsverse und der damit verbundenen "Servitisierung" und des "Manufacturing as a Service" - d.h. der Güterproduktion als Dienstleistung - ermöglicht die Hybride Montage außerdem eine flexiblere Arbeitsplanung.