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Global climate change represents a cognitive challenge for many people and often evokes negative associations due to its complexity and its interactions with politics, social movements, and economic developments. Consequently, the development and possession of green skills have become central to addressing climate change. The European Council conclusions acknowledge this urgency and underline the need for a transition towards green skills. This recognition also extends to higher education, where institutions play a crucial role in tackling the climate crisis. Personal Green Skills in Higher Education (PeGSinHE) is an Erasmus+ KA2 project coordinated by Kauno Kolegija (KK, Lithuania), Tampere University of Applied Sciences (TAMK, Finland), Hochschule für Agrar- und Umweltpädagogik (HAUP, Austria), Universidad de Málaga (UMA, Spain), and the Technical University of Applied Sciences Würzburg-Schweinfurt (THWS, Germany). The strategically designed project aims not only to promote green skills among students and encourage personal behavioural change in line with the Sustainable Development Goals, but also to foster a sense of social responsibility within the partner institutions. A particular focus lies on empowering lecturers at partner universities through innovative teaching and learning approaches to effectively impart green skills to students. This report documents the pilot implementation of an international 3 ECTS Blended Intensive Programme (BIP) developed within the EU project and delivered for the first time at the Technical University of Applied Sciences Würzburg-Schweinfurt in Germany. The programme is based on a curriculum co-created in focus groups and informed by a previously developed methodology, clearly defined learning objectives and learning outcomes, as well as the competencies of the participating lecturers. This report documents the pilot implementation of an international 3 ECTS Blended Intensive Programme (BIP) developed within the EU project and delivered for the first time at the Technical University of Applied Sciences Würzburg-Schweinfurt in Germany. The programme is based on a curriculum co-created in focus groups and informed by a previously developed methodology, clearly defined learning objectives and learning outcomes, as well as the competencies of the participating lecturers. The report provides an overview of the experiences gained during the BIP week in May 2025 and includes all relevant course descriptions, teaching materials, and learning content. It is intended to enable higher education institutions outside the project consortium to implement the BIP independently with individual adaptations.
This article examines the transformative effects of Smart Factory technologies - such as human-robot collaboration, intelligent assistance systems and cyber-physical production systems - on organizational design, with a particular focus on central fields of action for Human Resources management (HRM) and operational management. A case study of a German automotive supplier is used to examine how digitalization and automation are changing human work and organizational structures. Two future scenarios for organizational models are proposed: the swarm organization, which consists exclusively of highly qualified employees while robots take over routine tasks, and the polarized organization, which is characterized by a division between highly qualified specialists and low-skilled employees. Each scenario brings different challenges and opportunities for HR management, as companies need to adapt to digital skills, new models of collaboration and the management of a highly specialized or polarized workforce. This paper provides a conceptual framework and actionable insights for HRM and production management to manage the shift towards advanced, automated organizational models and ensure a smooth transition to the Smart Factory of the future.
Cost-oriented sensor concept for magnetostrictive force measurement and its material requirements
(2025)
Ferromagnetic materials change their magnetic properties under load, enabling the implementation of a force sensor. The magnetic field emerging from such a sensor can be measured by secondary sensors to approximate the load acting on the sensor. A test setup simulating a potential application environment is described and its measurement results are presented. Furthermore, relevant magnetic material properties of an exemplarily chosen cold working steel are discussed.
Der Beitrag stellt ein neuartiges, kostenorientiertes Konzept zur Kraftmessung basierend auf dem magnetostriktiven Wandlungsprinzip vor. Kernelement ist ein scheibenförmiger Sensor, der remanent magnetisiert ist und unter Last eine äußerlich durch Sekundärsensoren messbare Magnetfeldänderung erzeugt. Die magnetischen Eigenschaften eines marktüblichen Kaltarbeitsstahls werden hinsichtlich der sensorischen Eignung für dieses Konzept diskutiert und erste Ergebnisse bezüglich der Korrelation von Sekundärsensorsignalen zur aufgebrachten Last dargestellt.
Many popular piecewise regression models rely on minimizing a cost function on the model fit with a linear penalty on the number of segments. However, this penalty does not take into account varying complexities of the model functions on the segments potentially leading to overfitting when models with varying complexities, such as polynomials of different degrees, are used. In this work, we enhance on this approach by instead using a penalty on the sum of the degrees of freedom over all segments, called degrees-of-freedom penalized piecewise regression. We show that the solutions of the resulting minimization problem are unique for almost all input data in a least squares setting. We develop a fast algorithm that does not only compute a minimizer but also determines an optimal hyperparameter—in the sense of rolling cross validation with the one standard error rule—exactly. This eliminates manual hyperparameter selection. Our method supports optional user parameters for incorporating domain knowledge. We provide an open-source Python/Rust code for the piecewise polynomial least squares case which can be extended to further models. We demonstrate the practical utility through a simulation study and by applications to real data. A constrained variant of the proposed method gives state-of-the-art results in the Turing benchmark for unsupervised changepoint detection.
The body tracking systems on the current market offer a wide range of options for tracking the movements of objects, people, or extremities. The precision of this technology is often limited and determines its field of application. This work aimed to identify relevant technical and environmental factors that influence the performance of body tracking in industrial environments. The influence of light intensity, range of motion, speed of movement and direction of hand movement was analyzed individually and in combination. The hand movement of a test person was recorded with an Azure Kinect at a distance of 1.3 m. The joints in the center of the hand showed the highest accuracy compared to other joints. The best results were achieved at a luminous intensity of 500 lx, and movements in the x-axis direction were more precise than in the other directions. The greatest inaccuracy was found in the z-axis direction. A larger range of motion resulted in higher inaccuracy, with the lowest data scatter at a 100 mm range of motion. No significant difference was found at hand velocity of 370 mm/s, 670 mm/s and 1140 mm/s. This study emphasizes the potential of RGB-D camera technology for gesture control of industrial robots in industrial environments to increase efficiency and ease of use.
Abstract
Small load carriers (SLCs) are standardized reusable containers used to transport and protect customer goods in many manufacturers. Throughout the life cycle of the SLCs, they will be collected, manually checked for defects (wear, cracks, and residue on the surface), and cleaned by specialized logistic companies. Human operators in small to medium-sized companies manually evaluate the defects due to the variety and degree of possible defects and varying customer needs. This manual evaluation is not scalable and prone to errors. This work aims to fill this gap by proposing a computer vision system that can recognize the SLC type for inventory management and perform defect detection automatically. First, we develop a camera portal, consisting of standard components, that capture the relevant surfaces of the SLC. A labeled dataset of 17,530 images of 34 different SLCs with their defect status was recorded using this camera portal. We trained a classification model (ConvNeXt) using our dataset to predict the different types of SLCs achieving 100% class prediction accuracy. For defect detection, we explore eight state-of-the-art (SOTA) anomaly detection models that achieved high rankings in the MVTec industrial anomaly detection benchmark. These models are trained using default hyperparameters and the two highest-scoring models were chosen and fine-tuned. The best-fine-tuned models based on “Area under the Receiver Operating Characteristic Curve (AUROC)” are PatchCore (0.811) and DRAEM (0.748). These results indicate that there is still potential for improvement in the automation of defect detection of SLCs.
Computerized Numerical Control (CNC) plays an important role in highly autonomous manufacturing systems with multiple machine tools. The necessary Numerical Control (NC) programs to manufacture the parts are mostly written in standardized G-code. An a priori evaluation of the energy demand of CNC-based machine processes opens up the possibility of scheduling multiple jobs according to balanced energy consumption over a production period. Due to this, we present a combined Machine Learning (ML) and Job-Shop-Scheduling (JSS) approach to evaluate G-code for a CNC-milling process with respect to the energy demand of each G-command. The ML model training data are derived by the Latin hypercube sampling (LHS) method facing the main G-code operations G00, G01, and G02. The resulting energy demand for each job enhances a JSS algorithm to smooth the energy demand for multiple jobs, as peak power consumption needs to be avoided due to its expense.
Die Studie befasst sich mit den Herausforderungen und Chancen, die kleine und mittlere Unternehmen (KMU) in der Region Mainfranken, insbesondere in den Bereichen Maschinenbau und Automobilindustrie, im Zuge des Wandels der Arbeitswelt erleben. Ein zentrales Thema ist der Mangel an qualifizierten Arbeitskräften, der 89 % der befragten KMU betrifft. Die Unternehmen müssen attraktive Arbeitsbedingungen schaffen, um Talente zu gewinnen und zu halten, was zu einem "War for Talent" führt.
Die Studie hebt die Notwendigkeit hervor, kreative Ansätze zur Mitarbeiterbindung und -gewinnung zu entwickeln, wie flexible Arbeitszeiten und Weiterbildungsmöglichkeiten. Zudem wird betont, dass eine enge Zusammenarbeit mit Bildungseinrichtungen und die Entwicklung maßgeschneiderter Schulungsprogramme entscheidend sind, um den Anforderungen des Marktes gerecht zu werden und die Wettbewerbsfähigkeit der Region zu sichern.
The described data set contains features from the machine control of a five-axis milling machine. The features were recorded during thirteen series productions. Each series production includes a changeover process in which the machine was set up for the production of a different product. In addition to the timestamps and the twenty recorded features derived from Numerical Control (NC) variables, the data set also contains labels for the different production phases. For this purpose, up to 23 phases were assigned, which are based on a generalized milling process. The data set consists of thirteen .csv files, each representing a series production. The data set was recorded in a production company in the contract manufacturing sector for components with real series orders in ongoing industrial production.
Given a Caputo-type fractional differential equation with order between 1 and 2, we consider two distinct solutions to this equation subject to different sets of initial conditions. In this framework, we discuss nontrivial upper and lower bounds for the difference between these solutions. The main emphasis is on describing how such bounds are related to the differences of the associated initial values.
Global climate change is a cognitive challenge for many people and often evokes negative associations due to its complexity and interactions with politics, social movements and economic developments. Therefore, the possession of green skills becomes central to the fight against climate change. The European Council conclusions recognize this urgency and underline the need for a transition to green skills. This recognition also extends to higher education, where institutions have a crucial role to play in tackling the climate crisis. Personal Green Skills in Higher Education (PeGSinHE) is an Erasmus+ KA2 project coordinated by Kauno Kolegija (KK, Lithuania), Tampere University of Applied Sciences (TAMK, Finland), Hochschule für Agrar- und Umweltpädagogik (HAUP, Austria), Universidad de Málaga (UMA, Spain) and Technical University of Applied Sciences Würzburg Schweinfurt (THWS, Germany). The strategically designed project aims not only to promote green skills among students and encourage personal behavioral change in line with the Sustainable Development Goals, but also to instill a sense of social responsibility in the partner institutions. The focus is on empowering lecturers at partner universities through innovative teaching and learning methods to effectively impart green skills to students. This report describes the objectives and methodology used to assess environmental and sustainability competencies in the higher education institutions involved in the project. Methodologically, the report uses an assessment template designed to provide a comprehensive overview of best practice and baseline levels of environmental and sustainability competencies. It advocates the involvement of key stakeholders from all five partner Higher Education Institutions to ensure a broad perspective on these practices and competences within their respective countries and organizations. Different methods and perspectives will be used to collect data to enable a holistic understanding of the topic. The joint completion of the assessment template serves as a catalyst for joint discussions on the level of environmental and sustainability competencies and the identification of best practices in each organization. The results show that national implementation strategies are relatively loose, although some competency descriptions set targets for undergraduate degree programs. Challenges faced by higher education staff include resource constraints, particularly lack of time, the need for a deeper understanding of sustainable development and pedagogical tools, and the need for improved opportunities for collaboration. Given the time and resource constraints of this study, the results must be considered preliminary. Nevertheless, they confirm the findings of previous studies.
High-temperature calibration methods in additive manufacturing involve the use of advanced techniques to accurately measure and control the temperature of the build material during the additive manufacturing process. Infrared cameras, blackbody radiation sources and non-linear optimization algorithms are used to correlate the temperature of the material with its emitted thermal radiation. This is essential for ensuring the quality and repeatability of the final product. This paper presents the calibration procedure of an imaging system for in-situ measurement of absolute temperatures and temperature gradients during powder bed fusion of metal with laser beam (PBF-LB/M) in the temperature range of 500 K–1500 K. It describes the design of the optical setup to meet specific requirements in this application area as well as the procedure for accounting the various factors influencing the temperature measurement. These include camera-specific effects such as varying spectral sensitivities of the individual pixels of the sensor as well as influences of the exposure time and the exposed sensor area. Furthermore, influences caused by the complex optical path, such as inhomogeneous transmission properties of the galvanometer scanner as well as angle-dependent transmission properties of the f-theta lens were considered. A two-step fitting algorithm based on Planck's law of radiation was applied to best represent the correlation. With the presented procedure the calibrated thermography system provides the ability to measure absolute temperatures under real process conditions with high accuracy.
On the way to climate neutrality manufacturing companies need to assess the Carbon dioxide (CO2) emissions of their products as a basis for emission reduction measures. The evaluate this so-called Product Carbon Footprint (PCF) life cycle analysis as a comprehensive method is applicable, but means great effort and requires interdisciplinary knowledge. Nevertheless, assumptions must still be made to assess the entire supply chain. To lower these burdens and provide a digital tool to estimate the PCF with less input parameter and data, we make use of machine learning techniques and develop an editorial framework called MINDFUL. This contribution shows its realization by providing the software architecture, underlying CO2 factors, calculations and Machine Learning approach as well as the principles of its user experience. Our tool is validated within an industrial case study.
Computerized Numeric Control (CNC) plays an essential role in highly autonomous manufacturing systems for interlinked process chains for machine tools. NC-programs are mostly written in standardized G-code. Evaluating CNC-controlled manufacturing processes before their real application is advantageous due to resource efficiency. One dimension is the estimation of the energy demand of a part manufactured by an NC-program, e.g. to discover optimization potentials. In this context, this paper presents a Machine Learning (ML) approach to assess G-code for CNC-milling processes from the perspective of the energy demand of basic G-commands. We propose Latin Hypercube Sampling as an efficient method of Design of Experiments to train the ML model with minimum experimental effort to avoid costly setup and implementation time of the model training and deployment.
Computer Vision in Reusable Container Management - Requirements, Conception, and Data Acquisition
(2023)
In container management, the reuse of small load carriers is a business alternative to disposal carriers. Reusable container management is furthermore a solution to improve the environmental impact of the logistic industry. The sorting and stock management of small load carriers are today primarily manual work and have consequently a low level of automation. In order to increase the automation of returnable containers, it is crucial to establish a computer vision system that (i) classifies the containers and (ii) detects potential defects or stains. This paper provides an overview and a discussion of the applications that are already in use. Object detection is necessary for many actions in the container management business processes, such as inventory and stock management. Detection of defects on the small load carrier is required for scrapping the carriers to ensure a smooth process in any business process involving the carrier and to decide whether additional process steps, e.g., cleaning, are required. The literature review in this paper establishes the demand for computer vision detection and shows the project setup necessary to conduct research in this area. The comparison with other applications of defect and anomaly detection supports the applicability and shows the need for further research in this specific academic field. This leads to a project outline and the research provides the technical implementation of the detections in container management. Accordingly, the research provides a work- flow guide from data acquisition to a high-quality dataset of labeled anomalies of small load carriers.