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Automatisierte Fahrfunktionen haben sich fest in der Automobilindustrie etabliert. Insbesondere Einparkvorgänge lassen sich aufgrund der geringen Geschwindigkeiten und des vergleichsweise kleinen Betriebsraums sehr gut automatisieren. Um von den Insassen akzeptiert zu werden, muss eine Pkw-Parkfunktion jedoch souverän und mindestens so schnell agieren wie der Mensch. IPG Automotive, die Hochschule Kempten und MdynamiX zeigen, wie die eigenentwickelte Vehicle-in-the-Loop-Methode die durchgängige Bewertung automatisierter Fahrfunktionen unterstützen kann.
Obesity is one of the major challenges of modern societies. The food industry is under increasing pressure from government and the consumer side to reformulate foods to reduce the fat, sugar and salt content. However, if fat or sugar is reduced the overall flavor perception of the food is changed. In the case of fat reduction these changes are caused by a different aroma release during in mouth processing.
Additionally the texture is perceived as less creamy. It was recently found that aeration can increase aroma release and change texture perception in terms of creaminess.
The goal of this work is to study both effects in perspective of fat reduction in fermented dairy products.
Various mathematical models will be used to understand and describe the different effects involved. Since temperature is a critical parameter for aroma release and foam stability, heat transfer into a simplified mouth model for aroma release analysis was studied using the finite element method. In a second step the aroma release during foam collapse will be modelled and validated by aroma analysis data. In the last step the model linking the sensory perception of creaminess to the physical parameters of the foam (friction coefficient, viscosity, bubble size distribution) and the chemical attributes of the aroma substance (hydrophobicity, volatility) will be shown.
The findings from this work, both from the effect of aeration and the methodological approach may be transferred to other food categories as well.
Fat reduction is a major challenge for food manufacturers, because the desired calorie reduction goes along with a different aroma perception of low-fat products. To compensate for these changes, this work investigated the effect of foaming on aroma release from a fat-free dairy matrix. A mouth model and an analytical method based on HS-SPME-GC-IMS were designed and validated to quantify dynamic aroma release during thermally induced foam collapse. It was demonstrated that foaming significantly changes the release of aroma compounds. Depending on the hydrophobicity of the aroma substance, release was either increased or decreased after the inclusion of a gas phase.
Since aeration can increase aroma perception, it may be used to compensate the perceived aroma loss of low-fat dairy products. To be able to study the effect of aeration on aroma release systematically in the future, a temperature study was conducted on an in vitro model built to study aroma release at temperatures relevant for oral processing. The studied dairy foam contained 4% (w/w) milk protein and 0.75% (w/w) alginate and had a gas volume fraction of 71% after whipping. The temperature development during incubation of the foam at 40 °C was calculated using a numeric simulation. Thermal equilibrium was reached after 50 min. Sampling for dynamic aroma release was found to be ideal at 2, 7 and 15 min, because mean foam temperatures were 25, 30 and 35 °C. Experimental temperature analysis of the foam core was in good agreement with the simulation (R2 = 0.95).
When we are trying to decrease caloric intake by reducing fat content, aroma perception of the food is changed as well. Since previous studies indicated that foaming changes the aroma release, it was our goal to understand the physico-chemical background of this effect. Therefore, ten aroma compounds were added to a foamed acidified dairy matrix (4% milk protein, 1% gelatin, 60% gas volume). We simulated oral temperature conditions in a simplistic way through incubation at 40 °C and analyzed aroma release using headspace-solid phase microextraction-gas chromatography-ion mobility spectrometry. Significantly more highly hydrophobic aroma compounds were released from the foamed matrix than the unfoamed matrix, while compounds of intermediate hydrophobicity were released more from unfoamed matrix. The effect was independent from foam collapse and persisted for hours afterwards. Analytical results were complemented by orthonasal and retronasal sensory perception studies, which confirmed significant differences between aroma release behavior from foamed foods.
The production of food is highly complex due to the various chemo-physical and biological processes that must be controlled for transforming ingredients into final products. Further, production processes must be adapted to the variability of the ingredients, e.g., due to seasonal fluctuations of raw material quality. Digital twins are known from Industry 4.0 as a method to model, simulate, and optimize processes. In this vision paper, we describe the concept of a digital food twin. Due to the variability of the raw materials, such a digital twin has to take into account not only the processing steps but also the chemical, physical, or microbiological properties that change the food independently from the processing. We propose a hybrid modeling approach, which integrates the traditional approach of food process modeling and simulation of the bio-chemical and physical properties with a data-driven approach based on the application of machine learning. This work presents a conceptual framework for our digital twin concept based on explainable artificial intelligence and wearable technology. We discuss the potential in four case studies and derive open research challenges.
To understand aroma perception from complex food matrices‚ determination of dynamic aroma release during simulated oral processing is necessary. In this study optimization, validation and application of a novel method coupling headspace-solid phase microextraction (HS-SPME) with gas chromatography-ion mobility spectrometry (GC-IMS) is presented. Thirteen character impact compounds imparting different chemical properties are studied to understand capabilities and limitations of the method. It was shown for the first time that the temperature of the IMS sample inlet can be increased up to 200 °C without instrumental constraints. Linear calibration was possible for eleven of the thirteen compounds with one decade dynamic range. The limit of detection and quantitation were 2.1–63.0 ppb and 7.2–210.1 ppb, respectively. Diacetyl could be detected in negative polarity mode of IMS, however with lower precision compared to the compounds detected in positive mode. Limitations of the method were short HS-SPME extraction time, which in the case of caproic acid was not sufficient for reliable quantification. Additionally, δ-decalactone could not be detected due to maximum GC temperature of 200 °C. Application of the method to determine dynamic aroma release from a dairy matrix was successfully shown for nine compounds. Analysis of complex food matrix was performed with similar precision compared to analysis in aqueous solution, thus proving high robustness of the method towards matrix effects.
Eine Vielzahl von Produkten in den Supermarktregalen basiert auf Milch als Ausgangsprodukt. Dabei finden unterschiedliche Aufbereitungen der Rohmilch Anwendung. Der wohl wichtigste Prozess hierbei ist die Homogenisierung, welche vorrangig für die Stabilität des Produktes wichtig ist. An genau diesem Punkt kommt die Analyse der Partikelgröße ins Spiel.
Tensile properties of 2D braid reinforced composites are analyzed with regards to process-induced fiber architecture variations. First, a parametric study concerning the effect of radial braiding parameters on the braid characteristics is performed and a quantitative quality index based on visual attributes is introduced. Second, test laminates are produced with vacuum resin infusion to conduct tensile tests with biaxial and triaxial braids of varying qualities. To correlate fiber architecture variations with mechanical properties, each braided layer is scanned and analyzed by means of gray scale based image processing. This allows for determining specimen-specific fiber orientations and correlating them with mechanical properties. The results show strong correlations of measured fiber architectures and mechanical properties. Thus, the study suggests that detailed information about the (local) fiber architecture should allow for significantly improving the prediction of 2D braided composite properties.
This paper provides an approach for controlling the level of risk when operating highly automated transportation systems like cars, trains and similar. Such systems replace human perception and decision-making by employing highly sophisticated solutions based on electronics, IT, and AI. Such systems have demonstrated the potential for building highly automated vehicles, but, as of today, encounter challenges in correctly understanding the extremely complex open contexts, into which such vehicles could be deployed. Its key focus is on bounding the risks stemming from uncertainty in the perception of the environment.
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.
Large Language Models (LLMs) have become widely adopted recently. Research explores their use both as autonomous agents and as tools for software engineering. LLM-integrated applications, on the other hand, are software systems that leverage an LLM to perform tasks that would otherwise be impossible or require significant coding effort. While LLM-integrated application engineering is emerging as new discipline, its terminology, concepts and methods need to be established. This study provides a taxonomy for LLM-integrated applications, offering a framework for analyzing and describing these systems. It also demonstrates various ways to utilize LLMs in applications, as well as options for implementing such integrations.
Following established methods, we analyze a sample of recent LLM-integrated applications to identify relevant dimensions. We evaluate the taxonomy by applying it to additional cases. This review shows that applications integrate LLMs in numerous ways for various purposes. Frequently, they comprise multiple LLM integrations, which we term ``LLM components''. To gain a clear understanding of an application's architecture, we examine each LLM component separately. We identify thirteen dimensions along which to characterize an LLM component, including the LLM skills leveraged, the format of the output, and more. LLM-integrated applications are described as combinations of their LLM components. We suggest a concise representation using feature vectors for visualization.
The taxonomy is effective for describing LLM-integrated applications. It can contribute to theory building in the nascent field of LLM-integrated application engineering and aid in developing such systems. Researchers and practitioners explore numerous creative ways to leverage LLMs in applications. Though challenges persist, integrating LLMs may revolutionize the way software systems are built.
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.
Energy storage is a crucial flexibility measure to temporally decouple power generation from power demand and is touted as the missing link in realizing a decarbonized energy system based on renewable energy. Energy storage capacity buildup at all levels of the global energy system is expected to accelerate the decarbonization process. To this end, a coherent mathematical framework to ascertain the carbon footprint of localized energy systems with energy storage is indispensable. This article presents an open-source energy system simulation program — Energy System Network (ESN). A variety of energy system configurations can be simulated with the Python program, which incorporates key energy system components such as generation, grid, storage, and loads. ESN features an integrated bottom-up approach that combines energy system modeling with streamlined life cycle assessment techniques to quantify the carbon footprint of all components in a localized energy system. The lifecycle phases of each component, including production, operation, and end-of-life treatment, can be considered. Carbon footprint values are obtained for two demonstrative case studies with lithium-ion battery applications: energy arbitrage and home energy systems. The metric Levelized Emissions of Energy Supply (LEES) has been used to evaluate the carbon footprint of each application. An unconventional energy arbitrage strategy designed to exploit the grid carbon intensity spreads instead of the energy price spreads manages to achieve a LEES value about 17% lower than the conventional variant. The influence of rooftop solar generation, battery energy storage system, and the energy management strategy on the LEES values for a home energy system is explored. A maximum LEES reduction of over 37% vis-á-vis the base scenario was observed with optimal energy management for the solar generation and the battery system. The open-source availability of ESN can contribute to transparency, comparability, and reproducibility in carbon footprint assessments of localized energy systems with energy storage.
At the current development stage, the lower airspace above urban areas is only used to a very limited extent. Recent developments in the drone industry are making this area more accessible. The leading use case for drone applications is currently seen in the medical sector. Individual evidence shows that the use of drones to transport medical personnel for first medical response brings significant improvements in terms of cost and response time. Advantages in urban applications are seen as promising by some research projects, but this is not scientifically proven yet. This study deals with the simulation of transportation times of medical proposals by ambulance and drone in the metropolitan region of Stavanger and the comparison of transport times. The proposed methodology develops transferable results from concrete use-cases. By using a drone 80% of the expected operations can benefit from a reduction in transport time of up to 10 minutes with a variation of +/- 4 minutes through adjusted flight speeds due to weather conditions and variations in ambulance travel times due to different traffic volumes. The data set cleaned for the local special cases shows potential for a reduction of up to 20 minutes for the remaining operations, while the extracted individual cases even showing improvements of up to 60 minutes.
Dispatch of a grid energy storage system for arbitrage is typically formulated into a rolling-horizon optimization problem that includes a battery aging model within the cost function. Quantifying degradation as a depreciation cost in the objective can increase overall profits by extending lifetime. However, depreciation is just a proxy metric for battery aging; it is used because simulating the entire system life is challenging due to computational complexity and the absence of decades of future data. In cases where the depreciation cost does not match the loss of possible future revenue, different optimal usage profiles result and this reduces overall profit significantly compared to the best case (e.g., by 30–50%). Representing battery degradation perfectly within the rollinghorizon optimization does not resolve this—in addition, the economic cost of degradation throughout life should be carefully considered. For energy arbitrage, optimal economic dispatch requires a trade-off between overuse, leading to high return rate but short lifetime, vs. underuse, leading to a long but not profitable life. We reveal the intuition behind selecting representative costs for the objective function, and propose a simple moving average filter method to estimate degradation cost. Results show that this better captures peak revenue, assuming reliable price forecasts are available.
The electrification of transportation modes such as cars, buses, and boats offers the potential of providing vehicle-to-X services during idle times. Pools of vehicles can provide balancing power, trade on the electricity market, or be used for load peak shaving. In this work, the usage patterns of electric cars, electric buses, and electric boats are investigated, and the provision of vehicle-to-X with these vehicles is simulated using an open-source simulation tool. A data analysis and a vehicle usage pattern assessment show that especially private electric cars behave predictably at night. It also reveals that the vehicle-to-X availability varies over the week for all vehicle types and is highest at night for cars and buses. During the day on weekdays, private cars are available for vehicle-to-X 30 to 70% of the time, the analyzed buses 5 to 50% of the time, and the availability of the boats depends on their primary use as ferries or private boats. If the three transportation modes provide vehicle-to-X during idle times, the equivalent full cycles that the lithium-ion batteries complete increase at different rates depending on the vehicle pool size, while the mean charging rates decrease. Furthermore, an exemplary aging analysis shows that the additional load of vehicle-to-X provision slightly increases the capacity loss of the car batteries compared to a paused unidirectional charging strategy.
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.
Driving simulators are used to test under reproducible conditions, however, they must be validated for each application. This guarantees that the gathered data on the simulator is representative of real vehicle data. This paper examines and compares objective data from 20 drivers that are recorded on a six degrees of freedom (DOF) high dynamic driving simulator and a passenger vehicle in the compact class on a proving ground. The purpose of this study is to investigate the comparability of the behavior of the subjects in their driving task on the driving simulator compared to the real driving test. The driving maneuvers include the 18 m slalom and an ISO double lane change (ISO 3888-2). The real car’s measurement setup is composed of an inertial measurement unit and access to the chassis CAN messages. The driving simulator is equipped with the same real electrical power steering as the test vehicle. Furthermore, a fully validated vehicle model is used in the simulation. Objective key performance indicators such as maximum steering wheel angle, steering wheel torque, lateral acceleration, yaw rate, and yaw gain deviate from around -18% to 10% in the slalom, with the majority of parameters not showing significant differences. Bigger differences are found for the double lane change. Overall, the results demonstrate a satisfactory degree of correlation between the driver behavior on the driving simulator and the real vehicle, even up to achieving absolute validity.
Comfort evaluation on a dynamic driving simulator with advanced tire, road and vehicle models
(2024)
The topics of automated driving and digitization are becoming increasingly im-portant and will shape the future of mobility. The potential of this technology is enormous. Concurrently, manufacturers want to continue to differentiate them-selves in driving characteristics typical of their brands. Rapid developments re-garding technological changes as well as legal regulations combined with short development times present new challenges for the entire automotive industry. In this context, virtualization and front-loading methods play a major role within the vehicle development. There has been a clear trend of pushing virtual devel-opment via simulation to reduce the number of necessary prototypes. Since however, both engineers and management still rely heavily on the crucial in-sights gained by real road tests, subjective closed-loop assessment must remain a part of this virtual process. Driving simulators have the potential to bridge these gaps, allowing engineers and test drivers to subjectively experience and assess new systems in an early virtual phase of development.
Kempten University of Applied Sciences is working with research and technol-ogy partners to research and further develop their dynamic driving simulator. With the goal to develop use-case specific methods for virtual vehicle develop-ment, the simulator’s novel motion platform is used specifically for research projects in areas requiring high dynamic performance such as vehicle dynamics and ride. This paper describes the methods and solutions developed in an R&D project investigating the simulator’s capabilities for ride comfort evaluation, such as primary & secondary ride. With the goal to enable experienced test drivers to perform a subjective ride evaluation in a very early development phase, the simulator’s real-time environment was extended with the highly so-phisticated tire model FTire. This paper provides an overview of the system’s performance regarding subjective ride assessment. It presents a brief insight into the detailed road modelling and describes the measures taken to ensure real-time capability of the individual model interfaces. Objective performance evaluation shows the benefit of this work for comfort evaluation in early phases of virtual development.
Data Mining und Knowledge Discovery in Databases (KDD) sind Forschungs- und Anwendungsgebiete, die sich mit der Extraktion von nutzbarem Wissen aus Daten befassen. Dazu werden unter anderem Methoden des maschinellen Lernens eingesetzt. Die Induktive Logikprogrammierung ist ein Teilgebiet des Maschinellen Lernens, dessen Gegenstand das Lernen aus multi-relational und prädikatenlogisch repräsentierten Daten ist, während andere Lernverfahren üblicherweise Daten voraussetzen, die in Form einer einzelnen Attribut-Wert-Tabelle vorliegen. Der Einsatz von ILP-Methoden für KDD und Data Mining wird auch als Relationales Data Mining bezeichnet. Ein Anwendungsgebiet von KDD und Data Mining ist die Teilgruppenanalyse. Dabei wird eine Population von Fällen, die in einer Datenbank repräsentiert sind, nach besonders interessanten Teilgruppen der Population durchsucht, indem mögliche Teilgruppen generiert und mithilfe geeigneter Interessantheitsfunktionen bewertet werden. Die vorliegende Arbeit hat sich zum Ziel gesetzt, Methoden zur sicheren Beschränkung des Suchraums bei der Suche nach interessanten Teilgruppen in multi-relationalen Daten zu erarbeiten und zu evaluieren. Dazu wird ein Verfahren zur Suche nach interessanten Teilgruppen in multi-relationalen Datenbanken entwickelt, das verschiedene Methoden zur Suchraumbeschränkung integriert. Die verschiedenen Methoden zur Suchraumbeschränkung werden in Experimenten evaluiert und das entwickelte Verfahren zur Bearbeitung eines echten Data Mining-Problems eingesetzt. Die Arbeit bietet im einzelnen: (1) eine Formalisierung der Teilgruppenanalyse im Rahmen der ILP, (2) Optimumschätzfunktionen zu ausgewählten Interessantheitsfunktionen, (3) eine Erweiterung des bekannten Apriori-Suchverfahrens zur Warenkorbanalyse, die es erlaubt, die von Apriori durchsuchte Hypothesensprache einzuschränken, (4) einen ILP-Sprachbias für die Teilgruppenanalyse, der die Anwendung der Teilmengenbedingung des Apriori-Suchverfahrens zur Beschränkung eines ILP-Suchraums erlaubt, (5) einen SQL-Sprachbias für die Teilgruppenanalyse in multi-relationalen Datenbanken, (6) einen Ansatz zur Integration der Suchraumbeschränkung anhand von Taxonomien in einen Apriori-artigen Suchalgorithmus, (7) eine Methode zur Behandlung diskretisierter numerischer Attribute, die die Suchraumbeschränkung anhand von Allgemeinerbeziehungen zwischen Intervallen vereinheitlicht mit der Suchraumbeschränkung anhand von Taxonomien, (8) Experimente zur Wirksamkeit der verschiedenen Möglichkeiten zur Suchraumbeschränkung, (9) die Anwendung der entwickelten Ansätze auf ein echtes Data Mining-Problem mit Bank-Daten und ausführliche Vergleiche mit verwandten Arbeiten. Die Experimente wurden mit einer prototypischen Implementation der in dieser Arbeit entwickelten Ansätze durchgeführt. Dabei haben sich Teilmengenbedingung und Optimumschätzfunktionen als wirkungsvolle und zuverlässige Methoden zur Beschränkung des Suchraums erwiesen, während der Beitrag der Taxonomien zur Suchraumbeschränkung zwischen verschiedenen Anwendungen stark schwankte und in einigen Fällen nur gering war. Ein wichtiges Ergebnis der Versuche ist, daß die Teilmengenbedingung, die bisher nur zur Suchraumbeschränkung in ein-relationalen Datenbanken eingesetzt werden konnte, für multi-relationale Datenbanken und ILP-Sprachen genauso wirkungsvoll sein kann wie für ein-relationale Datenbanken.
VBA bietet das Potenzial, effektive Digitalisierungslösungen mit geringem Aufwand zu realisieren. “VBA für Office-Automatisierung und Digitalisierung" zeigt mit vielen Codebeispielen die Automatisierung von Excel, Word, Outlook, PowerPoint, SAP ERP und SOLIDWORKS und das Zusammenwirken dieser Systeme. Auch Webservices und Rest APIs werden mit VBA angesprochen und erschließen interessante Möglichkeiten bis hin zu KI. Das Buch erläutert wichtige Konzepte und gibt viele Tipps, um VBA-Anwendungen mit einfachen Mitteln unternehmenstauglich und administrierbar zu gestalten.
„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.
Die produzierende Industrie des DACH-Raumes erlebt im Jahr 2023 vielfältige Herausforderungen. Gerade scheint die Covid-Pandemie überwunden, welche die Vulnerabilität globaler Lieferketten schonungslos offenbart hat, treten neue Herausforderungen zutage. Veränderungen der gesetzlichen Anforderungen (wie EU Data Act), steigende Ansprüche an ökologische Nachhaltigkeit (z.B. Kreislaufwirtschaft) oder veränderte Kundenbedürfnisse (wie insbesondere Servitisierung) führen – bei einem konstant hohen Niveau an Variantenvielfalt – zu erheblichen technologischen Herausforderungen. Diese werden darüber vielerorts flankiert und im negativen Sinne überlagert durch einen erheblichen Mangel an Arbeits- und Fachkräften in der Produktion. Das Produktionssystem der Zukunft wird unserer Einschätzung nach daher nicht nur unternehmensübergreifenden Datenaustausch ermöglichen, Kreislaufwirtschaft befähigen und verstärkt kundenzentriert ausgerichtet sein. Es wird Arbeits- und Fachkräfte in der Produktion auf vielfältige Art und Weise „begeistern“. Auf diese Weise wird Fluktuation reduziert, generisches Wissen im Unternehmen gehalten und so die Grundlage für nachhaltigen Geschäftserfolg und technologischen Fortschritt sowie die langfristige Sicherung von attraktiven Arbeitsplätzen in der produzierenden Industrie geschaffen.
Nach unserer Überzeugung setzen sich in der produzierenden Industrie des DACH-Raumes diesbezüglich zwei Erkenntnisse durch:
1. Der „kritische Wettbewerb“ stammt selten aus Europa
2. Für die Bewerkstelligung dieser Herausforderungen sind authentische Impulse von außen – insbesondere von anderen, vergleichbaren Unternehmen – ein zentraler Erfolgsfaktor.
In dieser Gemengelage haben wir mit dem SUMMIT ALLGÄU eine Managementkonferenz „von der Industrie für die Industrie“ ins Leben gerufen. Im Zentrum des Veranstaltungskonzeptes stehen hierbei keine wissenschaftlichen Fachvorträge, sondern authentische Erfahrungsberichte hochkarätiger Referenten aus der industriellen Praxis. Beim ersten SUMMIT ALLGÄU Produktion am 23. und 24. Oktober 2023 in Marktoberdorf standen inhaltlich insbesondere die Themenkomplexe „Transformation & Nachhaltigkeit“, „Faktor Mensch in der Produktion“ sowie „Variantenvielfalt“ im Fokus. Auf überfachlicher Ebene wurden vor allem der persönliche Austausch und das Networking zwischen den zahlreichen Teilnehmern, Referenten und Ausstellern fokussiert.
Portable Emission Measurement Technology and RDE on Motorcycles as Instruments for Future Challenges
(2020)
The packaging of fresh meat has been studied for decades, leading to improved packaging types and conditions such as modified atmosphere packaging (MAP). While commonly used meat packaging uses fossil fuel-based materials, the use of biodegradable packaging materials for this application has not been studied widely. This study aimed at evaluating the sustainability of biodegradable packaging materials compared to established conventional packaging materials through analyses of the quality of freshly packaged pork. The quality was assessed by evaluating sensory aspects, meat color and microbiological attributes of the pork products. The results show no significant differences (p > 0.05) in ground pork and pork loin stored in biodegradable MAP (BioMAP) and conventional MAP for the evaluated sensory attributes, meat color or total bacterial count (TBC) over extended storage times. The data suggest that BioMAP could be a viable alternative to MAP using conventional, fossil fuel-based materials for the storage of fresh meats, while simultaneously fulfilling the customers’ wishes for a more environmentally friendly packaging alternative.
Die Rolle von Batteriespeichern im Kontext eines künftigen Energiesystems: Smart Energy Systems
(2023)
Welche Bedeutung haben Batteriespeicher für künftige Energiesysteme? Können wir mit Batteriespeichern eine sichere, stabile und bezahlbare Energieversorgung erreichen? Wie flexibel muss unser Stromsystem sein?
In seiner Präsentation im erläutert Prof. Dr. Holger Hesse anhand anschaulicher Beispiele und seiner jahrelangen Erfahrung, ob und wie Elektrofahrzeuge künftig auch durch "Vehicle-to-Grid" einen Beitrag leisten können, wann "Second-life" eine wirkliche Option sein kann und in welchen Bereichen es schon heute wirtschaftlich sein kann, einen Batteriespeicher zu betreiben.
Battery electric buses (BEBs) are gaining prominence in public transportation systems. In this paper, we investi-gate the impact of road grade, passenger load, and recuperation power limits on the energy consumption of BEBs using a physics-based model with tuned parameters. The model was employed to conduct a sensitivity analysis taking into account different altitude data sources, passenger load assumptions, and maximum recuperation power limits. The results highlight the importance of considering the route topology and its interaction with dynamic passenger loading for energy consumption predictions. Further-more, the results indicate that various altitude data sources are feasible to estimate the road grade for this purpose. Apart from that, the sensitivity for recuperation power limitations is shown and put into context. Within a broader framework, the findings suggest that physics-based energy consumption models with optimised parameters can serve as a powerful tool for enhanced operations and planning of BEBs.
AI in Battery Storage
(2023)
Energy storage is a crucial flexibility measure to temporally decouple power generation from power demand and is touted as the missing link in realizing a decarbonized energy system based on renewable energy. Energy storage capacity buildup at all levels of the global energy system is expected to accelerate the decarbonization process. To this end, a coherent mathematical framework to ascertain the carbon footprint of localized energy systems with energy storage is indispensable. This article presents an open-source energy system simulation program — Energy System Network (ESN). A variety of energy system configurations can be simulated with the Python program, which incorporates key energy system components such as generation, grid, storage, and loads. ESN features an integrated bottom-up approach that combines energy system modeling with streamlined life cycle assessment techniques to quantify the carbon footprint of all components in a localized energy system. The lifecycle phases of each component, including production, operation, and end-of-life treatment, can be considered. Carbon footprint values are obtained for two demonstrative case studies with lithium-ion battery applications: energy arbitrage and home energy systems. The metric Levelized Emissions of Energy Supply (LEES) has been used to evaluate the carbon footprint of each application. An unconventional energy arbitrage strategy designed to exploit the grid carbon intensity spreads instead of the energy price spreads manages to achieve a LEES value about 17% lower than the conventional variant. The influence of rooftop solar generation, battery energy storage system, and the energy management strategy on the LEES values for a home energy system is explored. A maximum LEES reduction of over 37% vis-á-vis the base scenario was observed with optimal energy management for the solar generation and the battery system. The open-source availability of ESN can contribute to transparency, comparability, and reproducibility in carbon footprint assessments of localized energy systems with energy storage.
Predictive battery life models are commonly utilized to extrapolate degradation trends observed during accelerated aging tests for simulation of degradation in real-world applications. Thus, fitting accelerated aging data as accurately as possible and with low uncertainty is crucial for making believable projections of battery lifetime, but it is challenging to identify algebraic expressions that accurately fit multivariate degradation trends. A review of models published in literature reveal some common expressions for fitting calendar aging data, which is only dependent on temperature and state-of-charge, but no consistency across many models for fitting cycle aging data, indicating the need for a statistically rigorous data driven approach for developing empirical models. This talk will describe a machine-learning assisted method for identification of predictive battery life models utilizing bilevel optimization and symbolic regression. Bilevel optimization with cross-validation is used to statistically determine cell- and stress-dependent model parameters, while symbolic regression identifies both linear and multiplicative candidate expressions to predict stress-dependent degradation rates by selecting low-order subsets of features from a generated feature library. Because model expressions are identified empirically, it is crucial to ensure resulting models behave according to physical expectations, so the stability of models for interpolation or extrapolation is interrogated qualitatively through simulation and quantitatively through cross-validation and uncertainty quantification via bootstrap resampling. This model identification approach substantially improves upon models identified purely using expert judgement in terms of both accuracy and uncertainty. Model simulation and validation is then conducted by deriving a state-equation form of the predictive model, enabling simulation of battery aging under dynamic stresses. This enables validation of the predictive battery model on lab-based tests with varying conditions or on drive-cycle or application-cycle testing protocols. Parameter uncertainty can be carried forward into model simulation, giving lifetime estimates and confidence windows for cell- or system-level lifetime. The financial impact of battery model uncertainty can be estimated by incorporating uncertainty into a technoeconomic model.
Das Wissen um den zukünftigen Wärmebedarf gewinnt, bei komplexer werdenden Fernwärmesystemen mit volatilen und heterogenen Erzeugungsanlagen, immer weiter an Bedeutung. Denn zur Vermeidung von ineffizienten und unwirtschaftlichen Betriebszuständen muss für eine passende Einsatzoptimierung eine möglichst präzise und zuverlässige Wärmelastprognose vorliegen. Maschinelle Lernverfahren, steigende Datengrundlagen und Verfügbarkeit von ausreichender Rechenleistung bergen hierbei erhebliches Verbesserungspotential. Das von der Hochschule für angewandte Wissenschaften Kempten durchgeführte Forschungsvorhaben „DeepDHC - Untersuchung und Weiterentwicklung modernster maschineller Lernverfahren für die hochgenaue Lastprognose in Fernwärmenetzen“ (FKZ: 03EN3017) befasste sich mit der Performance unterschiedlicher maschineller Lernverfahren zur Wärmelastprognose in Fernwärmenetzen. Unter Berücksichtigung unterschiedlicher Fernwärmenetztopologien, der Einbindung von Smart Meter Daten und der automatisierten Berücksichtigung von Veränderungen im Fernwärmenetz wurden hierbei besonders relevante Fragestellungen aufgegriffen und bearbeitet.
We study the action of the nonlinear mapping G[z] between real or complex Banach spaces in the vicinity of a given curve with respect to possible linearization, emerging patterns of level sets, as well as existing solutions of G[z]=0. The results represent local generalizations of the standard implicit or inverse function theorem and of Newton's Lemma, considering the order of approximation needed to obtain solutions of G[z]=0.
The main technical tool is given by Jordan chains with increasing rank, used to obtain an Ansatz, appropriate for transformation of the nonlinear system to its linear part. The family of linear mappings is restricted to the case of an isolated singularity.
Geometrically, the Jordan chains define a generalized cone around the given curve, composed of approximate solutions of order 2k with k denoting the maximal rank of Jordan chains needed to ensure k-surjectivity of the linear family. Along these lines, the zero set of G[z] in the cone is calculated immediately, agreeing up to the order of k−1 with the given approximation. Hence, the results may also be interpreted as a version of Tougeron's implicit function theorem or Hensel's Lemma in Banach spaces, essentially restricted to the arc case of a single variable.
Finally, by considering a left shift of the Jordan chains, the Ansatz can be modified in a systematic way to obtain a sequence of refined versions of linearization theorems and Newton Lemmas in Banach spaces.
Intensive research in the field over the past decades highlighted the complexity of aroma partition. Still, no general model for predicting aroma matrix interactions could be described. The vision outlined here is to discover the blueprint for the prediction of aroma partitioning behavior in complex foods by using machine learning techniques. Therefore, known physical relationships governing aroma release are combined with machine learning to predict the 𝐾𝑚𝑔 value of aroma compounds in foods of different compositions. The approach will be optimized on a data set of a specific food product. Afterward, the model should be transferred using explainable artificial intelligence (XAI) to a different food category to validate its applicability. Furthermore, we can transfer our approach to other relevant questions in the food field such as aroma quantification, extraction processes, or food spoilage.
The effect of ionomer to carbon (I/C) weight ratio and relative humidity (RH) on cathode catalyst degradation was investigated by comprehensive in situ characterization. Membrane electrode assemblies (MEA) with I/C ratios of 0.5, 0.8 and 1.2 were subjected to an accelerated stress test performed at 40, 70 and 100% RH. The results show an increasing loss in electrochemical active surface area (ECSA) for both higher I/C ratios and RH during voltage cycling. To differentiate between ionomer and water connected ECSA, carbon monoxide stripping measurements were performed at varying RH. Before degradation, all MEAs show comparable total ECSA values, while higher I/C ratios lead to a larger fraction of ionomer connected ECSA. After degradation, ECSA measurements of the lowest I/C ratio showed a relatively higher loss of Pt in contact with ionomer than Pt in contact with water, while an opposite trend was observed for higher I/C ratios. H2 /N2 impedance measurements showed drastically increasing protonic catalyst layer resistances for decreasing RH especially at low I/C ratios, which might hinder Pt 2+ ion diffusion towards the membrane, hence decreasing the ECSA loss. Limiting current measurements show increasing molecular O 2 diffusion resistances at end of test for samples with higher I/C ratios and higher ECSA loss.
As the adoption of battery electric buses (BEBs) in public transportation systems grows, the need for precise energy consumption forecasting becomes increasingly important. Accurate predictions are essential for optimizing routes, charging schedules, and ensuring adequate operational range. This paper introduces an innovative forecasting methodology that combines a propulsion and auxiliary energy model with a novel concept, the environment generator. This approach addresses the primary challenge in electric bus energy forecasting: estimating future environmental conditions, such as weather, passenger load, and traffic patterns, which significantly impact energy demand. The environment generator plays a crucial role by providing the energy models with realistic input data. This study validates various models with different levels of model complexity against real-world operational data from a case study of over one year with 16 electric buses in Göttingen, Germany. Our analysis thoroughly examines influencing factors on energy consumption, like altitude, temperature, passenger load, and driving patterns. In order to comprehensively understand energy demands under varying operational conditions, the methodology integrates data-driven models and physical simulations into a modular and highly accurate energy predictor. The results demonstrate the effectiveness of our approach in providing more accurate energy consumption forecasts, which is essential for efficient electric bus fleet management. This research contributes to the growing body of knowledge in electric vehicle energy prediction and offers practical insights for transit authorities and operators in optimizing electric bus operations.
Im vorliegenden Beitrag wird eine Methode zur subjektiven und objektiven Charakterisierung von aktiven Fahrstreifenwechselfunktionen sowie eine Korrelationsanalyse zur Ermittlung optimaler Funktionseigenschaften vorgestellt. Zur Quantifizierung maßgeblicher subjektiver Eigenschaften wurden Bewertungskategorien und -kriterien aus den Bereichen Fahrerkooperation, Funktionsperformance, Entlastungsgrad und Sicherheitsgefühl erarbeitet, deren Beurteilung im Rahmen einer umfassenden Fahrstudie erfolgte. Die beurteilten Fahrzeuge wurden hinsichtlich ihrer unterschiedlichen Funktionsausprägungen anschließend in einem neuartigen fahrmanöverbasierten Prüfverfahren vermessen. Das Verfahren umfasst hierbei drei Typen von Fahrstreifenwechselszenarien in welchen unter anderem die Eigen- und Relativbewegung von Ego- und Target-Fahrzeug sowie die Funktionsrückmeldung am Lenkrad und im Kombi-Instrument des Egofahrzeugs messtechnisch erfasst wurden. Die Auswertung des hiermit aufgezeichneten objektiven Funktionsverhaltens geschieht durch eine automatisierte KPI-basierte Softwareumgebung. Ausgehend von der korrelativen Gegenüberstellung aller Subjektivkriterien mit den ermittelten KPI-Kennwerten können wichtige Trends und Zusammenhänge geprüft, erkannt und nutzbringend in die Festlegung optimaler Wertbereiche eingearbeitet werden. Die vorgestellte Methodik ermöglicht somit eine zielgerichtete Auslegung und Abstimmung der Eigenschaften einer aktiven Fahrstreifenwechselfunktion.
Social Selling im Maschinen- und Anlagebau : Ein Fallbeispiel aus dem Bereich Antriebstechnik
(2023)
Social Selling: Ist das ein Hype oder ein neuer Vertriebsansatz? Diese Frage haben sich viele Geschäftsführer und Vertriebsmanager zu Beginn der Corona-Pandemie gestellt. Zu dem Zeitpunkt galt Social Selling, also die beziehungsorientierte Nutzung von sozialen Medien zur Erreichung von Vertriebszielen, als eine Lösung, um Kunden- kontakte trotz Reiserestriktionen zu ermöglichen. Viele Unternehmen haben ihre Vertriebsmitarbeiter im Umgang mit LinkedIn, der führendenden SocialMedia-Plattform im B2B-Geschäft, geschult. Nun stellt sich die Frage, ob diese Bemühungen erfolg reich waren und welche Erfolgsfaktoren für die Implementierung existieren. In dem Fallbeispiel aus dem Bereich Antriebstechnik werden die Vorgehensweise einer Implementierung und die Ergebnisse einer Befragung unter den Mitarbeitern vorgestellt. Damit lassen sich erste Rückschlüsse auf Erfolgsfaktoren für Social Selling in B2B-Unternehmen ziehen.
Decarbonisation of heat generation has become a priority for district heating network operators. In order to avoid the use of fossil-fired boilers, operators need to know peaks in heat demand in advance. Accurate thermal load forecasting is playing an increasingly important role in this respect. This paper presents the final results of the research project “deepDHC” (deep learning for district heating and cooling) funded by the German Federal Ministry for Economic Affairs and Climate Action (BMWK). The three-year project focused on systematically benchmarking thermal load forecasts for district heating networks, based on state-of-the-art machine learning methods. The analysis covers a variety of machine learning techniques, such as neural networks – including latest deep learning methods – (e.g. LSTM, TFT, ESN, RC), decision trees (random forests, adaptive boosting, XGB) and statistical methods (SARIMAX). In addition, the impact of combining methods by so-called “stacking” was investigated. Training and validation of the machine learning algorithms was based on historical operating data from the district heating network for the city of Ulm in Germany, in combination with historical weather data, and weather forecasts. Thermal load forecasts – typically for three days ahead – are presented and compared against one another. An automatic tuning routine was developed as part of the project, which enables regular re-training of the machine learning algorithms based on the latest operating data from the heating network. Furthermore, a web interface for real-time forecasting was developed and implemented at the power station.