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Influence of shear cutting parameters on the edge properties and the fatigue behavior of non‐oriented electrical steel sheets
Get PDFThe fatigue behavior of a fully processed, non‐oriented electrical steel sheet is investigated for different shear cutting parameters. Therefore, three cutting clearances (15 μm, 35 μm and 50 μm) in combination with two different punching tool wear states (sharp and worn) are compared regarding their mechanical properties. For this purpose, surface measurements, nanoindentation tests and stress‐controlled fatigue tests with a positive load ratio are performed for all six parameter sets. During shear cutting the material gets locally strain‐hardened and a deformed surface with micro‐notches is created. Compared to a polished reference condition, the fatigue strength of the shear‐cut sheets is severely deteriorated. However, the intensity of deterioration varies depending on the shear cutting parameters. For small cutting clearances, the highest fatigue life is observed for a sharp cutting tool. In contrast, for medium and high cutting clearances, samples that are cut with a worn tool achieve higher fatigue lives. Surface characteristics in the fracture zone, which act as a failure‐critical crack location, are considered as the main influencing factor.Das Ermüdungsverhalten eines vollständig schlussgeglühten, nicht kornorientierten Elektroblechs wird für verschiedene Parameterkombinationen während des Scherschneidens untersucht. Dazu werden drei Schnittspaltbreiten (15 μm, 35 μm und 50 μm) in Kombination mit zwei unterschiedlichen Verschleißzuständen der Stanzwerkzeugen (neuwertig und verschlissen) verglichen. Zu diesem Zweck werden für alle sechs Parametersätze Topographiemessungen, Nanohärtemessungen und spannungsgeregelte Ermüdungsversuche mit einem positiven Lastverhältnis durchgeführt. Die Schnittkanten sind als Folge des Scherschneidens lokal kaltverfestigt und es entsteht zudem eine raue Oberfläche mit Mikrokerben. Im Vergleich zu einem polierten Referenzzustand ist die Ermüdungsfestigkeit der schergeschnittenen Bleche dadurch verschlechtert. Die Intensität der Verschlechterung hängt jedoch von den Scherschneidparametern ab. Bei kleinen Schnittspalten wird die höchste
Ermüdungslebensdauer für einen neuwertigen Schneidwerkzeug beobachtet. Im Gegensatz dazu erreichen Proben, die mit einem verschlissenen Werkzeug geschnitten werden, bei mittleren und großen Schnittspalten eine höhere Ermüdungslebensdauer. Die Oberflächenbeschaffenheit in der Bruchzone, die als versagenskritische Anrissposition wirkt, wird hierbei als Haupteinflussfaktor angesehen
Antifragile Control Systems: The case of mobile robot trajectory tracking in the presence of uncertainty
Get PDFMobile robots are ubiquitous. Such vehicles benefit from well-designed and calibrated control algorithms ensuring their task execution under precise uncertainty bounds. Yet, in tasks involving humans in the loop, such as elderly or mobility impaired, the problem takes a new dimension. In such cases, the system needs not only to compensate for uncertainty and volatility in its operation but at the same time to anticipate and offer responses that go beyond robust. Such robots operate in cluttered, complex environments, akin to human residences, and need to face during their operation sensor and, even, actuator faults, and still operate. This is where our thesis comes into the foreground. We propose a new control design framework based on the principles of antifragility. Such a design is meant to offer a high uncertainty anticipation given previous exposure to failures and faults, and exploit this anticipation capacity to provide performance beyond robust. In the current instantiation of antifragile control applied to mobile robot trajectory tracking, we provide controller design steps, the analysis of performance under parametrizable uncertainty and faults, as well as an extended comparative evaluation against state-of-the-art controllers. We believe in the potential antifragile control has in achieving closed-loop performance in the face of uncertainty and volatility by using its exposures to uncertainty to increase its capacity to anticipate and compensate for such events
"When Two Wrongs Don't Make a Right" - Examining Confirmation Bias and the Role of Time Pressure During Human-AI Collaboration in Computational Pathology
Get PDFArtificial intelligence (AI)-based decision support systems hold promise for enhancing diagnostic accuracy and efficiency in computational pathology. However, human-AI collaboration can introduce and amplify cognitive biases, like confirmation bias caused by false confirmation when erroneous human opinions are reinforced by inaccurate AI output. This bias may increase under time pressure, a ubiquitous factor in routine pathology, as it strains practitioners’ cognitive resources. We quantified confirmation bias triggered by AI-induced false confirmation and examined the role of time constraints in a web-based experiment, where trained pathology experts (n=28) estimated tumor cell percentages. Our results suggest that AI integration fuels confirmation bias, evidenced by a statistically significant positive linear-mixed-effects model coefficient linking AI recommendations mirroring flawed human judgment and alignment with system advice. Conversely, time pressure appeared to weaken this relationship. These findings highlight potential risks of AI in healthcare and aim to support the safe integration of clinical decision support systems
Validity of Driver Assistance Systems in Driving Simulators: A Comparative Study of Real-World Driving and Two Simulator Environments
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Federated Learning via Decentralized Dataset Distillation in Resource Constrained Edge Environments
No full textIn federated learning, all networked clients contribute to the model training cooperatively. However, with model sizes increasing, even sharing the trained partial models often leads to severe communication bottlenecks in underlying networks, especially when communicated iteratively. In this paper, we introduce a federated learning framework FedD3 requiring only one-shot communication by integrating dataset distillation instances. Instead of sharing model updates in other federated learning approaches, FedD3 allows the connected clients to distill the local datasets independently, and then aggregates those decentralized distilled datasets (e.g. a few unrecognizable images) from networks for model training. Our experimental results show that FedD3 significantly outperforms other federated learning frameworks in terms of needed communication volumes, while it provides the additional benefit to be able to balance the trade-off between accuracy and communication cost, depending on usage scenario or target dataset. For instance, for training an AlexNet model on CIFAR-10 with 10 clients under non-independent and identically distributed (Non-IID) setting, FedD3 can either increase the accuracy by over 71% with a similar communication volume, or save 98% of communication volume, while reaching the same accuracy, compared to other one-shot federated learning approaches