@article{CosmaKesslerGebhardtetal.2020,
  author    = {Cosma, Cosmin and Kessler, Julia and Gebhardt, Andreas and Campbell, Ian and Balc, Nicolae},
  title     = {Improving the Mechanical Strength of Dental Applications and Lattice Structures SLM Processed},
  series = {Materials},
  volume    = {13},
  journal   = {Materials},
  number    = {4},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {1996-1944},
  doi       = {10.3390/ma13040905},
  pages     = {1 -- 18},
  year      = {2020},
  abstract  = {To manufacture custom medical parts or scaffolds with reduced defects and high mechanical characteristics, new research on optimizing the selective laser melting (SLM) parameters are needed. In this work, a biocompatible powder, 316L stainless steel, is characterized to understand the particle size, distribution, shape and flowability. Examination revealed that the 316L particles are smooth, nearly spherical, their mean diameter is 39.09 μm and just 10\% of them hold a diameter less than 21.18 μm. SLM parameters under consideration include laser power up to 200 W, 250-1500 mm/s scanning speed, 80 μm hatch spacing, 35 μm layer thickness and a preheated platform. The effect of these on processability is evaluated. More than 100 samples are SLM-manufactured with different process parameters. The tensile results show that is possible to raise the ultimate tensile strength up to 840 MPa, adapting the SLM parameters for a stable processability, avoiding the technological defects caused by residual stress. Correlating with other recent studies on SLM technology, the tensile strength is 20\% improved. To validate the SLM parameters and conditions established, complex bioengineering applications such as dental bridges and macro-porous grafts are SLM-processed, demonstrating the potential to manufacture medical products with increased mechanical resistance made of 316L.},
  language  = {en}
}
@article{WilbringEnningPfaffetal.2020,
  author    = {Wilbring, Daniela and Enning, Manfred and Pfaff, Raphael and Schmidt, Bernd},
  title     = {Neue Perspektiven f{\"u}r die Bahn in der Produktions- und Distributionslogistik durch Prozessautomation},
  series = {ETR - Eisenbahntechnische Rundschau},
  volume    = {69},
  journal   = {ETR - Eisenbahntechnische Rundschau},
  number    = {3},
  issn      = {0013-2845},
  pages     = {15 -- 19},
  year      = {2020},
  language  = {de}
}
@incollection{EngemannDuKallweitetal.2020,
  author    = {Engemann, Heiko and Du, Shengzhi and Kallweit, Stephan and Ning, Chuanfang and Anwar, Saqib},
  title     = {AutoSynPose: Automatic Generation of Synthetic Datasets for 6D Object Pose Estimation},
  series = {Machine Learning and Artificial Intelligence. Proceedings of MLIS 2020},
  booktitle = {Machine Learning and Artificial Intelligence. Proceedings of MLIS 2020},
  publisher = {IOS Press},
  address   = {Amsterdam},
  isbn      = {978-1-64368-137-5},
  doi       = {10.3233/FAIA200770},
  pages     = {89 -- 97},
  year      = {2020},
  abstract  = {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.},
  language  = {en}
}
@article{EngemannDuKallweitetal.2020,
  author    = {Engemann, Heiko and Du, Shengzhi and Kallweit, Stephan and C{\"o}nen, Patrick and Dawar, Harshal},
  title     = {OMNIVIL - an autonomous mobile manipulator for flexible production},
  series = {Sensors},
  volume    = {20},
  journal   = {Sensors},
  number    = {24, art. no. 7249},
  publisher = {MDPI},
  address   = {Basel},
  isbn      = {1424-8220},
  doi       = {10.3390/s20247249},
  pages     = {1 -- 30},
  year      = {2020},
  language  = {en}
}
@inproceedings{UlmerBraunChengetal.2020,
  author    = {Ulmer, Jessica and Braun, Sebastian and Cheng, Chi-Tsun and Dowey, Steve and Wollert, J{\"o}rg},
  title     = {Gamified Virtual Reality Training Environment for the Manufacturing Industry},
  doi       = {10.1109/ME49197.2020.9286661},
  pages     = {1 -- 6},
  year      = {2020},
  language  = {de}
}
@inproceedings{UlmerBraunChengetal.2020,
  author    = {Ulmer, Jessica and Braun, Sebastian and Cheng, Chi-Tsun and Dowey, Steve and Wollert, J{\"o}rg},
  title     = {Simulation und Verifikation komplexer Handarbeitsprozesse durch die Kombination von Virtual Reality und Augmented Reality im Single-Piece-Workflow},
  series = {AALE 2020, 17. Fachkonferenz Angewandte Automatisierungstechnik in Lehre und Entwicklung, Automatisierung und Mensch-Technik-Interaktion, Leipzig, DE, 4.-6.3.2020},
  booktitle = {AALE 2020, 17. Fachkonferenz Angewandte Automatisierungstechnik in Lehre und Entwicklung, Automatisierung und Mensch-Technik-Interaktion, Leipzig, DE, 4.-6.3.2020},
  isbn      = {978-3-8007-5180-8},
  pages     = {1 -- 4},
  year      = {2020},
  language  = {de}
}