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Source Code of DA-FDM API for semi-automated subject indexing
No full textThis is the source code for the DA-FDM API. The API provides access to a backend model that generated automated subject indexing suggestions for research data based on textual input such as full texts, abstracts, or description fields.
The API itself handles the communication and data exchange, while the underlying model performed the semantic analysis and concept mapping.
The model suggested relevant concepts and classifications from established knowledge systems DFG Classification System, GND and Wikidata.
This dataset includes the source code as well as the corresponding documentation
Replication Data for: "Ring-Expansion Metathesis Polymerization under Confinement"
No full textThis dataset contains experimental data for the above mentioned publication in form of txt files for the GPC, BET and MALDI-TOF data as well as bruker folders for the obtained NMR data. The data is structured according to measurement type and substructured by pore sizes as well as reaction conditions where applicable
Digital-Twin Data for Large-Scale 3D Printing
No full text
MTRL_DATASET - Material Analysis Tools
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Overview
This project contains a dataset from research on a data-informed digital twin for large-scale 3D printing. The data was collected through a series of experiments using two machines: (i) a Kuka KR50R2500 industrial robot and (ii) a MAI® MULTIMIX-3D mortar mixing pump. Additionally, measurements of the printed object's width were recorded during the experiments.
The dataset has been used to explore correlations between machine performance, material behavior, and the final printed structure. The accompanying codebase enables replication of the study’s results, offering tools for data processing, clustering, visualization, and the analysis of various material properties and printing parameters.
The experiments in this study were conducted in two phases:
Correlation Analysis: The first phase focused on exploring the relationships between machine performance, material behavior, and the resulting printed object. Data from these experiments was then used to develop a clustering-based prediction model and a set of feedback control services to automate the operation of the Kuka robot and the mortar mixing pump.
Evaluation of Feedback Control: In the second phase, the feedback control services were implemented in a new set of experiments to assess their effectiveness in optimizing the 3D printing process.
Project Structure
MTRL_DATASET/
│
├── Data/
│ ├── Block_Tests/
│ │ ├── BlockTasks.json - Tasks data for block (evaluation ) tests
│ │ └── BlockMeasurements.json - Width measurements for different block types
│ │
│ └── Mixture_Experiments/
│ ├── PumpResponse_Clean.json - Clean pump response data for mixture experiments (used for clustering)
│ ├── PumpTasks.json - Task data for pump operations
│ └── WidthCorrelationTests.json - Data for width correlation tests
│
├── Clusters/ - Directory for saved ML models
│ ├── kmeans.pkl - Trained KMeans model
│ └── scaler.pkl - Fitted StandardScaler
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├── Figs/ -Directory of all plots from the code
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├── Blocks.py - Block data analysis and visualization
├── BlockMeasurements.py - Analysis of block measurement data
├── Clusters.py - Machine learning clustering of pump data
├── PumpData.py - Pump data analysis utilities
├── WidthCorrelations.py - Analysis of correlations between printed object width, Kuka robot velocity and pump reqeuncy
└── README.md - This file
Data Description
01_Mixture Experiments Data
The experiments in this section were done with 4 types of mixtures as follows:
Mix
Clay (kg)
Sand (kg)
Water (kg)
Flow Table (mm)
Density (g/L)
M1
5.00
7.50
3.00
169
2080
M2
5.00
7.50
2.75
163
2100
M3
5.00
7.50
2.50
147
2140
M4
5.00
7.50
2.25
127
2196
PumpResponse_Clean.json
Contains cleaned pump response data including:
Pump output power and current measurements
Mortar temperature readings
Pump pressure readings
Temporal data for pump operations
PumpTasks.json
Contains task information related to pump operations:
Task details for mixture experiments
Timing information for pump actions
Operational parameters and settings
WidthCorrelationTests.json
Contains test data for analyzing correlations between:
Width measurements
Pump frequency
Robot velocity
02_Block Tests Data
The experiments in this section were conducted on 3 block types with the following features:
Block
Velocity (m/s)
Frequency (Hz)
B1
0.11
17
B2
Adaptive
17
B3
0.11
Adaptive
BlockMeasurements.json
Contains width measurements (in mm) for three different blocks. This data is used for comparing width consistency across different block printing strategies.
BlockTasks.json
Contains detailed task data from the pump and the robot for block printing processes, including:
Task IDs and names
Task types (ReadData, Read, SetValue)
Actors (KukaPassiveRead, MaiPrinter)
Timestamps for start and end times
Job identification (e.g., "BLOCK1")
Processing levels and indices
The file contains over 2300 task entries.
03_Features
Within both the BlockTasks.json and PumpTask.json files, every data point adheres to a standardized Task Data Schema that includes the following fields:
_id: A unique identifier for this record.
task_id: A unique identifier for the specific task or operation.
name: The name assigned to the task or process.
type: The category or type of operation (e.g., a data reading action).
main_actor: The primary machine or module responsible for carrying out the task.
description: A brief textual description of the task.
message: A log message or status note associated with the operation.
element_id: A list of identifiers for related design elements (if any).
actors_data: Contains information for the actors involved in the task (nested details are omitted).
job: The job or process identifier associated with this record.
level: The hierarchical level or depth in a process, indicating its relative position.
index: A numerical order or position indicator for the task.
start_time: The timestamp marking the start of the task or operation.
end_time: The timestamp marking when the task was completed.
response: Contains the outcome or data returned by the task (nested details are omitted).
versions: Version control or metadata information regarding this record.
project: The identifier for the project to which this record belongs.
author: The user or entity that created or is responsible for this record.
04_Scripts
PumpData.py
Analyzing and visualising pump tasks data for the 4 mixtures.
WidthCorrelations.py
Analyzes and visualises correlations between width measurements, robot velocity, and pump frequency.
Clusters.py
Implements machine learning clustering on pump data and visualises the results.
Blocks.py
Analyzes task data for different block types, calculates timing metrics, and visualises the results.
BlockMeasurements.py
Analyzes and visualises block width measurement data.
05_Usage
Dependencies
This project requires the following Python packages:
pandas
numpy
matplotlib
scikit-learn
joblib
Install dependencies with:
pip install pandas numpy matplotlib scikit-learn joblib
Run the code
To generate the full analysis and plots from the dataset, simply execute the main.py file. Open your terminal, navigate to the code directory, and run:
cd path/to/your/code/directory
python main.py
For a more detailed overview of available parameters, run each of the individual Python files separately.
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Interview Transcripts on Conditions For Study Success (WiSe 2024/25)
No full textThis dataset contains raw interview data (transcripts) from a seminar on study success requirements ("Lernerfolgsbedingungen im Hochschulstudium", Universität Stuttgart) in winter 2024/25. Students are narrating their experiences with a wide range of topics regarding their learning and study conditions, and their impact on study success.The interviews are best characterized as problem-centered interviews with strong narrative elements. The interviews were conducted online via the WebEx platform or in person
Numerical Data for: Numerical simulation of droplet impact onto a smooth heated substrate
No full textIn this work, we studied FC-72 (refrigerant perfluorohexane) droplet impact onto a hot calcium fluoride (CaF2) substrate to observe both solid cooling and droplet spreading, thereby gaining insight into the heat transfer mechanism during the
spreading process. The surface is super-hydrophobic, which corresponds to a contact angle of 180°.This study employs a Computational Fluid Dynamics (CFD) framework to simulate the impact dynamics using the Finite Volume method. The interface is defined by the Volume of Fluid (VOF) method and the Piecewise Linear Interface Calculation (PLIC) method. The Direct Numerical Simulation (DNS) tool Free Surface 3D (FS3D), an in-house code at the Institute of Aerospace Thermodynamics, University of Stuttgart, is utilized.3D simulations are performed for a quarter of the droplet. An FC-72 droplet of size 1.02 mm impacts with an impact velocity of 0.26 m/s onto a super-hydrophobic calcium fluoride surface. The solid substrate is maintained at 342K, which is 13K higher than the saturation temperature of the liquid (FC-72). Both the droplet and the ambient gas are at 293K. This corresponds to a Weber number of 14 and a Reynolds number of 955. The energy equation is solved using a single temperature field and phase change is ignored. As the surface is flat, no cell contains all three phases (solid, liquid and gas). The computational domain consists of (128*128*256) cells, which corresponds to 60 cells per initial droplet diameter. Images are available in a separate directory, for different time instances, stored as time_ms.png. Two simulation videos are stored for the temperature distribution, one for the dimensionless temperature (T) of the system and one with absolute temperature.The data is saved in HDF5 files. The simulation data for 20ms is stored in 50 timesteps. For each timestep, the VOF variable (funs00*.hdf), solid volume (fun300*.hdf), pressure (pres00*.hdf), temperature (temp00*.hdf) and velocity (velv00*.hdf) are stored. VOF is a scalar field with values between 0 and 1, indicating the phases between ambient gas (air) as 0, and drop liquid (FC-72) as 1, and the interface in between. The results are provided starting from the moment of droplet impact onto the surface.The simulation was performed as part of the GRK 2160 within the subproject SP-B5
Visualizations from A Mesh-Free Multi-Physics Approach to Modeling Deep-Hole Drilling and Predicting Chip Jamming and Drill Failure
No full textDeep-hole drilling
Deep-hole drilling (DHD), particularly at micro scales, presents significant challenges in experimental investigations due to inaccessibility and complex fluid-structure interaction.
The related dissertation introduces a novel mesh-free multi-physics modeling approach that couples Smoothed Particle Hydrodynamics (SPH) and the Discrete Element Method to simulate transient chip evacuation and investigate failure mechanisms such as chip jamming and drill breakage. This modeling approach enables the detailed representation of dynamic interfaces and boundary conditions inherent in DHD, which are otherwise difficult to capture using traditional mesh-based methods.
Video Context
The presented mesh-free modeling approach allows the transient investigation of the chip evacuation in DHD. The dissertation demonstrates the approach capabilities for investigating the impact of drill geometry, metalworking fluid (MWF) characteristics, borehole filling states, and MWF supply levels on the chip evacuation efficiency.
The videos provided show the simulation results presented in the related dissertation
Extended Visual Analysis System for Scene-Graph-Based Visual Question Answering
No full textSource code of our extended visual analysis system to explore scene-graph-based visual question answering. This approach is built on top of the state-of-the-art GraphVQA framework which was trained on the GQA dataset.
Additionally, it is an improved version of our system that can be found here
Instructions on how to use our system can be found in the README
Supplementary Data for Paper "Laser-plume interactions in deep-penetration remote laser welding of stainless steel"
No full textThis repository is related to the paper "Laser-plume interactions in deep-penetration remote laser welding of stainless steel" published in the Journal of Optics and Laser Technology. The repository contains a video of the high-speed Schlieren imaging of the vapour plume, as well as the videos of the emission of the vapour plume with different filter configurations. Additionally, the corresponding spectrometer data plotted in the publication are given
Linked RDF graphs of an architectural and structural representation of a timber structure
No full textThis dataset provides a collection of semantically enriched RDF graphs representing both architectural and structural aspects of a timber structure. The data is organized into modular Turtle (.ttl) files and one RIF rule definition (.txt) for reasoning purposes.
Included in the dataset:
Architectural Graph.ttl: Captures the spatial and material characteristics of the architectural model, defined using standard RDF vocabularies.
Structural Graph.ttl: Encodes structural elements and their relationships, including support systems and load-bearing components, structured for semantic querying.
Neutral Building Model (Architectural + Structural).ttl: A consolidated RDF representation integrating architectural and structural elements. All proprietary references (e.g., BHoM) have been removed to ensure vendor neutrality and interoperability. Architectural columns are linked to structural bars, and architectural floors to structural panels. The model is fully queryable using SPARQL and adheres to open-access, GDPR-compliant standards.
RDF_RIF_Rule.pie.txt: A rule expressed in RDF/RIF Core syntax that demonstrates reasoning capabilities on the dataset.
This dataset supports the findings of a related journal paper (currently under submission) and is complemented by a GitHub repository containing the scripts and tools used to generate the RDF data. It is intended for researchers and professionals working on Linked Building Data, semantic modeling, ontology design, and integrated architectural/structural workflows in BIM.
All files are formatted using open standards (RDF, Turtle, RIF) and designed for use in FAIR-compliant, interdisciplinary design environments
Replication Data for: Development of a Basalt Fiber-Reinforced Composite Duct for Post-Tensioned Functionally Graded Concrete Structures
No full textThis dataset represents an addendum and contains relevant data on the development and testing of a mineral-based composite duct for the placement of unbonded tendons in functionally graded concrete components. The composite duct possesses a tube profile, was produced in a pultrusion process and constists of basalt reinforcing fibers, embedded in a matrix of chemically bonded phosphate ceramics (CBPC).
The dataset is structured in the following topics:
dimensions/
To examine the manufacturing quality, the following geometric dimensions of the individual test specimens were taken using a caliper gauge (precision 0.01 mm) and compared with a target value: Inner diameter and wall thickness at the two end points as well as outer diameter in the middle and end area. Based on those values, the respective cross-section was derived. For better comparability, the measured values are illustrated in a spider diagram. To account and check for manufacturing consistency, the test specimens were picked from different areas of the pultrusion process - namely Start, Mid and End.
experimental_results/
The experimental results from uniaxial tensile tests (DIN EN ISO 527-4 and -5) and three-point-bending tests (DIN EN ISO 14125) allow an assessment of the material behavior during loading and up to fracture. The test setups and relevant geometric dimensions of the test specimens are provided in the enclosed figures. Each test series consisted of six specimens. This enabled a statistical evaluation based on mean values, standard deviations and coeffients of variation (COV). The dataset comprises information on the distance [mm] and force [N] measured by the testing machine (Zwick Type 1474) as well as the calculated stresses [N/mm²] and strains [%].The associated pictures and scanning electron microscope (SEM) images of fractured specimens facilitate a detailed analysis of the failure modes.
material_properties/
To further examine the manufacturing quality of the pultruded composite duct the dataset comprises: 1) SEM images with annotations of specimens from the three pultrusion areas (Start, Mid and End) prior to loading, and 2) pictures after a casting test with a highly flowable concrete to account for the impermeability of the duct.
To analyze the processability and curing behavior of the CBPC matrix, rheological investigations were performed with a plate-plate rheometer (Physica MCR 401). The collected data includes the shear rate [1/s] and stress [Pa], as well as the time [min], temperature [°C] and viscosity [Pa*s] for the test samples RS1 and RS2.
production_setup/
In addition to the metrological studies, the experimental tests and the investigations into manufacturing quality and processability, the dataset also contains relevant information on the design of the pultrusion line and the modifications to the impregnation setup based on schematic illustrations.
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