620 Ingenieurwissenschaften und zugeordnete Tätigkeiten
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Die Prüfung der Technischen Sauberkeit stellt in der Automobilindustrie einen wesentlichen Bestandteil der Qualitätssicherung dar. Gleichzeitig sind die zugehörigen Prüfprozesse häufig durch hohe manuelle Aufwände, komplexe Abläufe und unzureichend standardisierte Planungsgrundlagen gekennzeichnet. Ziel dieser Arbeit ist die Entwicklung eines methodischen Leitfadens zur systematischen Analyse, Bewertung und Optimierung industrieller Prüfprozesse zur Technischen Sauberkeit. Hierzu werden Methoden des Qualitätsmanagements, Prozessmanagements und der Arbeitswissenschaft zu einem ganzheitlichen Vorgehensmodell kombiniert. Der Leitfaden umfasst die strukturierte Erfassung und Modellierung bestehender Prozesse mittels geeigneter Methoden der Prozessmodellierung (z. B. ereignisgesteuerte Prozessketten und Swimlane-Darstellungen), die Durchführung eines REFA-basierten Arbeitsstudiums zur Ermittlung von Zeitanteilen und Vorgabezeiten sowie die Analyse von Störgrößen, Stillstandszeiten und weiteren nicht wertschöpfenden Prozessbestandteilen. Ergänzend werden Risiken hinsichtlich der Belastbarkeit von Planungsdaten bewertet und Optimierungspotentiale systematisch identifiziert. Die Anwendbarkeit des entwickelten Vorgehens wird anhand eines industriellen Prüfprozesses im Umfeld der Technischen Sauberkeit exemplarisch demonstriert. Dabei werden betriebliche Besonderheiten einer durchgeführten Fallstudie bei der LINAMAR Antriebstechnik GmbH abstrahiert und die gewonnenen Erkenntnisse in allgemein gültige Handlungsempfehlungen überführt. Das Ergebnis ist ein praxisorientierter Best-Practice-Leitfaden, der Unternehmen bei der Standardisierung, Bewertung und kontinuierlichen Verbesserung von Prüfprozessen unterstützt. Die Arbeit leistet einen Beitrag zur methodischen Weiterentwicklung der Prozessanalyse im industriellen Qualitätswesen und schafft eine Grundlage für effizientere und besser planbare Prüfprozesse im Bereich der Technischen Sauberkeit.
AI-driven risk estimation: a GPT-based approach to news monitoring for manufacturing resilience
(2026)
In today’s rapidly evolving commercial landscape, manufacturing enterprises face significant challenges in maintaining resilience amid disruptions such as pandemics, natural disasters, and geopolitical conflicts. To address these challenges, we introduce a novel GPT-based early detection tool designed for real-time supply chain risk assessment. This system integrates proprietary company data, including supply chain portfolios, with publicly available information, such as news articles, to estimate risk scores for respective supply chains, thereby enhancing decision-making processes. Leveraging advanced machine learning techniques–Generative Pretrained Transformers (GPT), zero-shot learning, and structured outputs–the tool operates locally to ensure data privacy and minimize information leakage. Utilizing the "news-please" crawler and the "Llama 3.1" GPT model, the system continuously monitors selected media sources, providing timely risk assessments. Our research demonstrates the tool’s potential to enhance proactive risk management in supply chains, validated through testing on both real and augmented datasets. By evaluating four exemplary supply chains, we characterize the tool’s capability to support decision-making in unpredictable global environments. The results indicate that, while the system occasionally exhibits oversensitivity, it consistently aids in identifying critical events that may impact supply chain operations. Future developments will focus on refining the tool’s accuracy and expanding its applications, particularly in monitoring regulatory changes.
Low-code approaches can accelerate decision-making in the semiconductor industry by streamlining simulation-driven insights. This supports the paradigm shift to Industry 4.0 and Industry 5.0 by enabling rapid development and optimized workflows. However, existing simulation methods often require extensive coding expertise, limiting accessibility and slowing down model development. This paper presents a simulation template that streamlines the development of discrete event simulation models in semiconductor manufacturing. Thus, the simulation template implements reusable components to simplify model creation and reduce development time. The approach encourages collaboration between technical and nontechnical stakeholders. Combined with a low-code data farming framework, the simulation template increases agility, accelerates experimentation, and supports efficient, data-driven production planning decisions.
Mechanische Erscheinungen
(2025)
The paper investigates the change of the structural dynamic behavior caused by embedding dry carbon fibers into beam-shaped specimens. It was assumed that a significant part of the vibration energy is dissipated due to friction between the dry fibers. To verify this, and to separate the effects of mass and stiffness changes, three different types of specimens – with and without dry fibers – were designed, manufactured and dynamically characterized through a bending resonance test. The results show a significant increase in damping due to the embedding of dry carbon fibers. Contrary to expectations, the natural frequency of this type of specimen increased along with the damping. However, the reason for this increase in damping and natural frequency remains unclear, as the decay curves, for example, do not exhibit a friction-typical characteristic.
Das Projekt PulLoop entwickelt kostengünstige, berührungslose Messsysteme für kontinuierliche Produktionsverfahren wie Pultrusion und Extrusion. Ziel ist die Integration eines optischen Messsystems zur Reduzierung von Ausschuss und zur datengetriebenen Prozessoptimierung. Der Closed-Loop-Ansatz ermöglicht eine effiziente Produktion mit präziser Toleranzdokumentation.
Using magnetic nanoparticles for extracorporeal magnetic heating applications in bio-medical technology allows higher external field amplitudes and thereby the utilization of particles with higher coercivities (HC). In this study, we report the synthesis and characterization of high coercivity cobalt ferrite nanoparticles following a wet co-precipitation method. Particles are characterized with magnetometry, X-ray diffraction, Mössbauer spectroscopy, transmission electron microscopy (TEM) and calorimetric measurements for the determination of their specific absorption rate (SAR). In the first series, CoxFe3−xO4 particles were synthesized with x = 1 and a structured variation of synthesis conditions, including those of the used atmosphere (O2 or N2). In the second series, particles with x = 0 to 1 were synthesized to study the influence of the cobalt fraction on the resulting magnetic and structural properties. Crystallite sizes of the resulting particles ranged between 10 and 18 nm, while maximum coercivities at room temperatures of 60 kA/m for synthesis with O2 and 37 kA/m for N2 were reached. Magnetization values at room temperature and 2 T (MRT,2T) up to 60 Am2/kg under N2 for x = 1 can be achieved. Synthesis parameters that lead to the formation of an additional phase when they exceed specific thresholds have been identified. Based on XRD findings, the direct correlation between high-field magnetization, the fraction of this antiferromagnetic byphase and the estimated transition temperature of this byphase, extracted from the Mössbauer spectroscopy series, we were able to attribute this contribution to akageneite. When varying the cobalt fraction x, a non-monotonous correlation of HC and x was found, with a linear increase of HC up to x = 0.8 and a decrease for x > 0.8, while magnetometry and in-field Mössbauer experiments demonstrated a moderate degree of spin canting for all x, yielding high magnetization. SAR values up to 480 W/g (@290 kHz, 69 mT) were measured for immobilized particles with x = 0.3, whit the external field amplitude being the limiting factor due to the high coercivities of our particles.
