07 Fakultät Konstruktions-, Produktions- und Fahrzeugtechnik

Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/8

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Start date: 2023Subject: search.filters.subject.004

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    SmartLab vernetzt Produktionsmaschinen : Aufbau einer digitalen Prozesskette in einer bestehenden Produktionsumgebung
    (2023) Schneider, Matthias; Meier, Veronika; Stehle, Thomas; Möhring, Hans-Christian
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    Fehlertolerante Sicherheitssteuerung aus der Cloud : Softwarebasierte Sicherheitssteuerungen
    (2023) Fischer, Marc; Walker, Moritz; Lechler, Armin; Riedel, Oliver; Verl, Alexander
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    Combining brain-computer interfaces with deep reinforcement learning for robot training : a feasibility study in a simulation environment
    (2023) Vukelić, Mathias; Bui, Michael; Vorreuther, Anna; Lingelbach, Katharina
    Deep reinforcement learning (RL) is used as a strategy to teach robot agents how to autonomously learn complex tasks. While sparsity is a natural way to define a reward in realistic robot scenarios, it provides poor learning signals for the agent, thus making the design of good reward functions challenging. To overcome this challenge learning from human feedback through an implicit brain-computer interface (BCI) is used. We combined a BCI with deep RL for robot training in a 3-D physical realistic simulation environment. In a first study, we compared the feasibility of different electroencephalography (EEG) systems (wet- vs. dry-based electrodes) and its application for automatic classification of perceived errors during a robot task with different machine learning models. In a second study, we compared the performance of the BCI-based deep RL training to feedback explicitly given by participants. Our findings from the first study indicate the use of a high-quality dry-based EEG-system can provide a robust and fast method for automatically assessing robot behavior using a sophisticated convolutional neural network machine learning model. The results of our second study prove that the implicit BCI-based deep RL version in combination with the dry EEG-system can significantly accelerate the learning process in a realistic 3-D robot simulation environment. Performance of the BCI-based trained deep RL model was even comparable to that achieved by the approach with explicit human feedback. Our findings emphasize the usage of BCI-based deep RL methods as a valid alternative in those human-robot applications where no access to cognitive demanding explicit human feedback is available.
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    Prediction of in-flight particle properties and mechanical performances of HVOF-sprayed NiCr-Cr3C2 coatings based on a hierarchical neural network
    (2023) Gui, Longen; Wang, Botong; Cai, Renye; Yu, Zexin; Liu, Meimei; Zhu, Qixin; Xie, Yingchun; Liu, Shaowu; Killinger, Andreas
    High-velocity oxygen fuel (HVOF) spraying is a promising technique for depositing protective coatings. The performances of HVOF-sprayed coatings are affected by in-flight particle properties, such as temperature and velocity, that are controlled by the spraying parameters. However, obtaining the desired coatings through experimental methods alone is challenging, owing to the complex physical and chemical processes involved in the HVOF approach. Compared with traditional experimental methods, a novel method for optimizing and predicting coating performance is presented herein; this method involves combining machine learning techniques with thermal spray technology. Herein, we firstly introduce physics-informed neural networks (PINNs) and convolutional neural networks (CNNs) to address the overfitting problem in small-sample algorithms and then apply the algorithms to HVOF processes and HVOF-sprayed coatings. We proposed the PINN and CNN hierarchical neural network to establish prediction models for the in-flight particle properties and performances of NiCr-Cr3C2 coatings (e.g., porosity, microhardness, and wear rate). Additionally, a random forest model is used to evaluate the relative importance of the effect of the spraying parameters on the properties of in-flight particles and coating performance. We find that the particle temperature and velocity as well as the coating performances (porosity, wear resistance, and microhardness) can be predicted with up to 99% accuracy and that the spraying distance and velocity of in-flight particles exert the most substantial effects on the in-flight particle properties and coating performance, respectively. This study can serve as a theoretical reference for the development of intelligent HVOF systems in the future.
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    Energy efficiency in ROS communication : a comparison across programming languages and workloads
    (2025) Albonico, Michel; Cannizza, Manuela Bechara; Wortmann, Andreas
    Introduction: The Robot Operating System (ROS) is a widely used framework for robotic software development, providing robust client libraries for both C++ and Python. These languages, with their differing levels of abstraction, exhibit distinct resource usage patterns, including power and energy consumption–an increasingly critical quality metric in robotics.
    Methods: In this study, we evaluate the energy efficiency of ROS two nodes implemented in C++ and Python, focusing on the primary ROS communication paradigms: topics, services, and actions. Through a series of empirical experiments, with programming language, message interval, and number of clients as independent variables, we analyze the impact on energy efficiency across implementations of the three paradigms.
    Results: Our data analysis demonstrates that Python consistently demands more computational resources, leading to higher power consumption compared to C++. Furthermore, we find that message frequency is a highly influential factor, while the number of clients has a more variable and less significant effect on resource usage, despite revealing unexpected architectural behaviors of underlying programming and communication layers.
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    Augmented reality to visualize a finite element analysis for assessing clamping concepts
    (2024) Maier, Walther; Möhring, Hans-Christian; Feng, Qi; Wunderle, Richard
    This paper presents the development of an innovative augmented reality application for evaluating clamping concepts through visualizing the finite element analysis. The focus is on transforming the traditional simulation results into immersive, holographic displays, enabling users to experience and assess finite element analysis in three dimensions. The application development process involves data processing by MATLAB, visualization in the software Unity, and displaying holograms through Microsoft’s Hololens2. The most significant advancement introduces a new algorithm for rendering different finite elements in Unity. The application targets not only university engineering students but also vocational students with limited background in finite element analysis and machining, aiming to make the learning process more interactive and engaging. It was tested in a real machining environment, demonstrating its technical feasibility and potential in engineering education.
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    Ein Vorgehensmodell zum systematischen Planen und Aufsetzen von Data Science Projekten
    (Stuttgart : Fraunhofer Verlag, 2024) Dukino, Claudia; Hölzle, Katharina (Prof. Dr. rer. oec. habil., MBA)