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An approach for the thermal modelling of machines and machine building interaction for the dynamic assessment of thermal loading conditions
No full textThe development of sustainable manufacturing systems necessitates the knowledge of the thermal loading conditions of the manufacturing equipment in interaction with the manufacturing building. Especially for the early planning phases a dynamic evaluation of different concepts is advantageous and leads to an acceleration of the overall planning procedure. This article introduces an approach for the development of simulation models for the assessment of thermal loading conditions in brownfield manufacturing applications by dynamic simulation. The approach makes use of the novel Modelica Thermal Integration Library, which provides basic models for representative manufacturing equipment in the metal working industry. It enables an efficient simulation of manufacturing equipment with the related manufacturing building to analyse the overall thermal loading conditions. The approach is applied to a machine tool in the manufacturing line at the ETA Research Factory of the Technical University of Darmstadt
Improving Daily Routine Recognition in Hearing Aids Using Sequence Learning
No full textThis work focuses on sequence learning to improve the daily routine recognition in hearing aids (HA), where the goal is to personalize the device configuration for each user. We apply the sequence methods on two large real-world data sets. One publicly available set contains the acceleration (ACC) data of one person, Huynh, over seven working days, whereas our set includes the real life of seven subjects over 104 days with ACC and audio data of a HA. For both sets, we design statistical features to represent the recurring routine behavior well. In our comprehensive simulations, we analyze several sequence classifiers learning the temporal relationships of high-level activities. The multi-layer perceptron (MLP) and random forest (RF) as an observation model for the hidden Markov model (HMM) show the best F-measure performance of 85.3% and 91.6% on our set and the Huynh set, respectively. In particular, the MLP-HMM combination strongly improves on both sets compared to the non-sequence classifier MLP by 6.7% and 10.2%. Within the segment error analysis, we show that the sequence classifiers improve the temporal prediction stability by a reduction of insertion errors. Thus, the improved sequence classification helps the user to better address his condition due to preferred HA settings
Measuring the resilience-efficiency trade-off: an empirical application for retail logistics
No full textHuman–robot vs. human–manual teams: Understanding the dynamics of experience and performance variability in picker-to-parts order picking
No full textElucidating on the drop impact dynamics of water-in-oil microemulsions via advanced rheometry
No full textMicroemulsions are promising for diverse applications, but their complex rheology, especially at high shear rates, poses characterization challenges. This study investigates the impact of water-in-oil microemulsion drops on flat and curved surfaces, focusing on the critical role of high-shear rheology. We assessed rheological properties across a wide range of shear rates by combining conventional methods, high-frequency rheometry, and a recently developed drop-impact viscometry (DIV) technique. DIV captures a substantial apparent viscosity increase (up to 12-fold) at high shear rates, enabling the prediction of maximum spreading and splashing thresholds. We observed complete splash suppression at high-impact velocities, where splashing is typically severe. These insights into high-shear rheological properties are crucial for optimizing microemulsion formulation and performance in applications such as spray and fuel injection
Medical Robotics for Engineering Undergraduates Through an Affordable Hands-on Lab Experiment
No full textDespite the increasing importance of medical
robotics in modern healthcare, these complex, precise, and costly
systems are nearly absent in practical teaching environments for
engineering and technology students. We present a scalable and
affordable hands-on experiment for undergraduates to address
this gap, which teaches the fundamentals of this essential and
rapidly growing technology through the example of a robotassisted
liver biopsy. Using a custom 3D-printed robotic arm with
parts costing less than USD1,000, students are guided through
tasks that begin with navigating the robotic arm’s workspace and
understanding its kinematics. They explore how rotational joint
movement creates linear motion at the end-effector before using
a biopsy needle with force measurement capabilities. Students
program the robotic arm to perform a controlled needle insertion
into a liver phantom, recording forces to distinguish between
tissue layers and locate a tumor model. During the experiment,
students are encouraged to experiment with different programming
and data analysis methods. We conducted a transfer test
to assess the effectiveness of our educational approach, in which
students who completed the experiment performed the task 21%
faster than those who did not, despite 27% of the students
having less prior experience in programming or robotics. This
suggests increased engagement and improved application of the
skills learned. By significantly reducing costs, this lab experiment
enhances accessibility to medical robotics training, preparing
students to address future challenges in healthcare
Sensor for Bilateral Human Bite Force Measurements
No full textBite force is an important characteristic of the
masticatory system’s functional state. Especially, force asymmetries
are potential indicators for malfunctions such as temporomandibular
disorders or dysgnathia. By measuring bilaterally,
i.e. simultaneously on the left and right side, it is possible to
quantify asymmetries. Currently, there is a lack of bite force
sensors combining a low measurement uncertainty (less than
5%) with the capability of measuring bilaterally. We present
a 1000N nominal bite force sensor with a height of 9 mm, which
enables bilateral measurements over a wide range of mouth
openings. The sensor is based on four load cells which are placed
between two bite forks. The dimensions of these forks build upon
anthropomorphic data of the human dental arch and are designed
such that the bite force is transmitted by the two premolar and
the first molar teeth. The developed sensor is characterized using
a universal testing machine, resulting in a linearity error of ±
1.2% full scale. An asymmetric application of force is quantifiable
with an error less than 4.1% from 100N on. Therefore, the bite
force sensor builds a promising basis for medical studies aiming
at the support of diagnosis and therapy with objective data
Current challenges in environmental transportation research
No full textThe global transportation sector continues to develop contrary to environmental needs. While other energy demand sectors in developed countries show a reversing trend in their greenhouse gas (GHG) emissions, transport is still lagging behind in meeting its climate targets (WRI, 2025). On the global scale, this is mainly driven by increasing motorization rates in developing countries due to economic growth, but also by the inability of developed countries to ban combustion vehicles as main transport means.
To meet GHG emission targets, a significant change in transport is needed–yet viable pathways to environmentally friendly transportation are vague. To identify suitable scenarios, an interdisciplinary research approach is required. Jointly, researchers can influence decisionmakers for shaping the global transport transition (cf. Stechmesser et al., 2024)
High-precision collinear laser spectroscopy at the Collinear Apparatus for Laser Spectroscopy and Applied Physics (COALA)
No full textCOALA is a new offline collinear laser spectroscopy setup for high-precision measurements and development work at TU Darmstadt, Germany. An introduction to the technique and the experimental setup is given and an overview of current projects with recent results is presented. The idea of a novel all-optical absolute charge radius determination is discussed
Seed size and pubescence facilitate secondary dispersal by dung beetles
No full textIn tropical forests, primary dispersal by animals is the most important form of seed dispersal. Dung beetles are secondary seed dispersers attracted to mammal feces. When they bury dung of frugivorous mammals, they move seeds to new sites, possibly protecting them from seed predation or pathogens, or moving to better micro‐climates and away from conspecifics. As a result, secondary dispersal by dung beetles potentially increases rates of seed survival and germination. Previous studies examined how dung beetles filter seeds by size. However, other seed traits have not been examined. We discovered that pubescent seeds covered with hairs on their surface hold a thin layer of dung and possibly “trick” the dung beetles into burying them like a dung ball. In a lowland tropical forest (Chocó Ecuador), we collected dung balls from dung beetles ( Canthon angustatus , Oxysternon conspicillatum , Sulcophanaeus noctis , and Scybalocanthon trimaculatus ), and fecal samples from brown‐headed spider monkeys ( Ateles fusciceps fusciceps ) and mantled howler monkey ( Alouatta palliata ). We characterized the traits of seed morphospecies found within samples and counted them. Our data show that larger size is coupled with a higher proportion of pubescence in seeds. The association between seed size and pubescence may extend beyond our study area supported by an analysis of the literature data for neotropical seeds at the genus level. Large pubescent seeds were more likely to be included in dung balls than smooth large seeds. Our results are consistent with the hypothesis that secondary dispersal by dung beetles exerts some selection pressure on the phenotype of endozoochorous seeds