Institute of Engineering and Computational Mechanics

The Institute of Engineering and Computational Mechanics focuses on questions relating to Technical Dynamics. This is the guideline for many research projects and courses. The research work of the institute is dedicated to technical dynamics in the broadest sense. In the course of time, changing focal points have developed. The research areas include multibody systems, contact mechanics, mechatronics, driving safety and uncertainties.

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11 to 20 of 301 Results

df_anomal_test_data_brute_force.csv Jul 6, 2026 - Replication data for: Data-Driven Anomaly Detection for Graph-based Formation Control in a Robot Swarm Comma Separated Values - 49.2 MB - MD5: 9b4b5b0d5c8b61289ff028afbab70e07 Test dataset of simulation runs containing one anomal robot with brute force motion behavior.
df_anomal_test_data_formation_changing.csv Jul 6, 2026 - Replication data for: Data-Driven Anomaly Detection for Graph-based Formation Control in a Robot Swarm Comma Separated Values - 49.8 MB - MD5: 29ca99ca594b0b5d46a363345669a1c3 Test dataset of simulation runs containing one anomal robot with formation-changing motion behavior.
df_anomal_test_data_noisy.csv Jul 6, 2026 - Replication data for: Data-Driven Anomaly Detection for Graph-based Formation Control in a Robot Swarm Comma Separated Values - 49.5 MB - MD5: 3572c9b0437879c19cc1c26cd5e6e116 Test dataset of simulation runs containing one anomal robot with noisy motion behavior.
df_anomal_test_data_swinging.csv Jul 6, 2026 - Replication data for: Data-Driven Anomaly Detection for Graph-based Formation Control in a Robot Swarm Comma Separated Values - 51.1 MB - MD5: 1b33ba4e6716c66d8563da7f27abc9ab Test dataset of simulation runs containing one anomal robot with swinging motion behavior.
df_normal_test_data.csv Jul 6, 2026 - Replication data for: Data-Driven Anomaly Detection for Graph-based Formation Control in a Robot Swarm Comma Separated Values - 50.3 MB - MD5: db4a1ff76c52a9503518582dd1c7804e Test dataset of simulation runs containing only normally behaving robots.
df_training_data.csv Jul 6, 2026 - Replication data for: Data-Driven Anomaly Detection for Graph-based Formation Control in a Robot Swarm Comma Separated Values - 50.1 MB - MD5: 30ddda3925445717370551505b237d85 Training dataset of simulation runs containing only normally behaving robots.
df_validation_data.csv Jul 6, 2026 - Replication data for: Data-Driven Anomaly Detection for Graph-based Formation Control in a Robot Swarm Comma Separated Values - 5.0 MB - MD5: e53ee451ed928098fbed5166234658f5 Validation dataset of simulation runs containing only normally behaving robots.
Simulation Results from Numerical Investigation of Tool Geometries in Friction Stir Welding of Aluminum Alloy 2024-T351 Using Smoothed Particle Hydrodynamics May 8, 2026 Kilittuwa Gamage, Ishan Anjana; Pimentel, Rodolfo; Baumann, Andreas; Eberhard, Peter, 2026, "Simulation Results from Numerical Investigation of Tool Geometries in Friction Stir Welding of Aluminum Alloy 2024-T351 Using Smoothed Particle Hydrodynamics", https://doi.org/10.18419/DARUS-5935, DaRUS, V1 Friction Stir Welding (FSW) is a state-of-the-art solid-state joining technique that has gained increasing attention in recent years due to its ability to produce high-quality welds in lightweight alloys. It uses a non-consumed rotating tool to generate frictional heat, softening the material, and stirring it, resulting in material joining by mixin...
Figure4_Nugget_zone_formation_of_the_standard_simulation.mp4 May 8, 2026 - Simulation Results from Numerical Investigation of Tool Geometries in Friction Stir Welding of Aluminum Alloy 2024-T351 Using Smoothed Particle Hydrodynamics MPEG-4 Video - 31.3 MB - MD5: c7e30f42997d27afde9cc99ac8ab8a0d Figure 4: Standard simulation: material flow and nugget zone formation over time at 𝑥 = 0 mm. The blue and green colors represent the advancing and retreating side, respectively.
Figure5_Comparison_of_with_threads_and_without_threads.mp4 May 8, 2026 - Simulation Results from Numerical Investigation of Tool Geometries in Friction Stir Welding of Aluminum Alloy 2024-T351 Using Smoothed Particle Hydrodynamics MPEG-4 Video - 48.7 MB - MD5: 4cfb842ebf3236647fdb27581f898c35 Figure 5: Comparison of velocity fields for treaded and non-threaded tools.

df_anomal_test_data_brute_force.csv

Jul 6, 2026 - Replication data for: Data-Driven Anomaly Detection for Graph-based Formation Control in a Robot Swarm

Comma Separated Values - 49.2 MB -

Test dataset of simulation runs containing one anomal robot with brute force motion behavior.

df_anomal_test_data_formation_changing.csv

Jul 6, 2026 - Replication data for: Data-Driven Anomaly Detection for Graph-based Formation Control in a Robot Swarm

Comma Separated Values - 49.8 MB -

Test dataset of simulation runs containing one anomal robot with formation-changing motion behavior.

df_anomal_test_data_noisy.csv

Jul 6, 2026 - Replication data for: Data-Driven Anomaly Detection for Graph-based Formation Control in a Robot Swarm

Comma Separated Values - 49.5 MB -

Test dataset of simulation runs containing one anomal robot with noisy motion behavior.

df_anomal_test_data_swinging.csv

Jul 6, 2026 - Replication data for: Data-Driven Anomaly Detection for Graph-based Formation Control in a Robot Swarm

Comma Separated Values - 51.1 MB -

Test dataset of simulation runs containing one anomal robot with swinging motion behavior.

df_normal_test_data.csv

Jul 6, 2026 - Replication data for: Data-Driven Anomaly Detection for Graph-based Formation Control in a Robot Swarm

Comma Separated Values - 50.3 MB -

Test dataset of simulation runs containing only normally behaving robots.

df_training_data.csv

Jul 6, 2026 - Replication data for: Data-Driven Anomaly Detection for Graph-based Formation Control in a Robot Swarm

Comma Separated Values - 50.1 MB -

Training dataset of simulation runs containing only normally behaving robots.

df_validation_data.csv

Jul 6, 2026 - Replication data for: Data-Driven Anomaly Detection for Graph-based Formation Control in a Robot Swarm

Comma Separated Values - 5.0 MB -

Validation dataset of simulation runs containing only normally behaving robots.

Simulation Results from Numerical Investigation of Tool Geometries in Friction Stir Welding of Aluminum Alloy 2024-T351 Using Smoothed Particle Hydrodynamics

May 8, 2026

Kilittuwa Gamage, Ishan Anjana; Pimentel, Rodolfo; Baumann, Andreas; Eberhard, Peter, 2026, "Simulation Results from Numerical Investigation of Tool Geometries in Friction Stir Welding of Aluminum Alloy 2024-T351 Using Smoothed Particle Hydrodynamics", https://doi.org/10.18419/DARUS-5935, DaRUS, V1

Friction Stir Welding (FSW) is a state-of-the-art solid-state joining technique that has gained increasing attention in recent years due to its ability to produce high-quality welds in lightweight alloys. It uses a non-consumed rotating tool to generate frictional heat, softening the material, and stirring it, resulting in material joining by mixin...

Figure4_Nugget_zone_formation_of_the_standard_simulation.mp4

May 8, 2026 - Simulation Results from Numerical Investigation of Tool Geometries in Friction Stir Welding of Aluminum Alloy 2024-T351 Using Smoothed Particle Hydrodynamics

MPEG-4 Video - 31.3 MB -

Figure 4: Standard simulation: material flow and nugget zone formation over time at 𝑥 = 0 mm. The blue and green colors represent the advancing and retreating side, respectively.

Figure5_Comparison_of_with_threads_and_without_threads.mp4

May 8, 2026 - Simulation Results from Numerical Investigation of Tool Geometries in Friction Stir Welding of Aluminum Alloy 2024-T351 Using Smoothed Particle Hydrodynamics

MPEG-4 Video - 48.7 MB -

Figure 5: Comparison of velocity fields for treaded and non-threaded tools.

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