TY  - CHAP
A1  - Engemann, Heiko
A1  - Du, Shengzhi
A1  - Kallweit, Stephan
A1  - Ning, Chuanfang
A1  - Anwar, Saqib
T1  - AutoSynPose: Automatic Generation of Synthetic Datasets for 6D Object Pose Estimation
T2  - Machine Learning and Artificial Intelligence. Proceedings of MLIS 2020
N2  - We present an automated pipeline for the generation of synthetic datasets for six-dimension (6D) object pose estimation. Therefore, a completely automated generation process based on predefined settings is developed, which enables the user to create large datasets with a minimum of interaction and which is feasible for applications with a high object variance. The pipeline is based on the Unreal 4 (UE4) game engine and provides a high variation for domain randomization, such as object appearance, ambient lighting, camera-object transformation and distractor density. In addition to the object pose and bounding box, the metadata includes all randomization parameters, which enables further studies on randomization parameter tuning. The developed workflow is adaptable to other 3D objects and UE4 environments. An exemplary dataset is provided including five objects of the Yale-CMU-Berkeley (YCB) object set. The datasets consist of 6 million subsegments using 97 rendering locations in 12 different UE4 environments. Each dataset subsegment includes one RGB image, one depth image and one class segmentation image at pixel-level.
Y1  - 2020
SN  - 978-1-64368-137-5
U6  - http://dx.doi.org/10.3233/FAIA200770
N1  - Frontiers in Artificial Intelligence and Applications. Vol 332
SP  - 89
EP  - 97
PB  - IOS Press
CY  - Amsterdam
ER  - 
TY  - CHAP
A1  - Ulmer, Jessica
A1  - Braun, Sebastian
A1  - Cheng, Chi-Tsun
A1  - Dowey, Steve
A1  - Wollert, Jörg
T1  - Gamified Virtual Reality Training Environment for the Manufacturing Industry
Y1  - 2020
U6  - http://dx.doi.org/10.1109/ME49197.2020.9286661
SP  - 1
EP  - 6
ER  - 
TY  - CHAP
A1  - Ulmer, Jessica
A1  - Braun, Sebastian
A1  - Cheng, Chi-Tsun
A1  - Dowey, Steve
A1  - Wollert, Jörg
T1  - Simulation und Verifikation komplexer Handarbeitsprozesse durch die Kombination von Virtual Reality und Augmented Reality im Single-Piece-Workflow
T2  - AALE 2020, 17. Fachkonferenz Angewandte Automatisierungstechnik in Lehre und Entwicklung, Automatisierung und Mensch-Technik-Interaktion, Leipzig, DE, 4.-6.3.2020
Y1  - 2020
SN  - 978-3-8007-5180-8
SP  - 1
EP  - 4
ER  - 
TY  - CHAP
A1  - Schmidt, Thomas
A1  - Kasch, Susanne
A1  - Eichler, Fabian
A1  - Thurn, Laura
T1  - Process strategies on laser-based melting of glass powder
T2  - Lasers in Manufacturing Conference 2021
N2  - This paper presents the laser-based powder bed fusion (L-PBF) using various glass powders (borosilicate and quartz glass). Compared to metals, these require adapted process strategies. First, the glass powders were characterized with regard to their material properties and their processability in the powder bed. This was followed by investigations of the melting behavior of the glass powders with different laser wavelengths (10.6 µm, 1070 nm). In particular, the experimental setup of a CO2 laser was adapted for the processing of glass powder. An experimental setup with integrated coaxial temperature measurement/control and an inductively heatable build platform was created. This allowed the L-PBF process to be carried out at the transformation temperature of the glasses. Furthermore, the component’s material quality was analyzed on three-dimensional test specimen with regard to porosity, roughness, density and geometrical accuracy in order to evaluate the developed L-PBF parameters and to open up possible applications.
KW  - 3D-printing
KW  - glass
KW  - additive manufactureing
KW  - laser based powder fusion
Y1  - 2021
ER  -