@incollection{FranzenSteckenPfaffetal.2019,
  author    = {Franzen, Julian and Stecken, Jannis and Pfaff, Raphael and Kuhlenk{\"o}tter, Bernd},
  title     = {Using the Digital Shadow for a Prescriptive Optimization of Maintenance and Operation : The Locomotive in the Context of the Cyber-Physical System},
  series = {Advances in Production, Logistics and Traffic},
  booktitle = {Advances in Production, Logistics and Traffic},
  publisher = {Springer},
  address   = {Cham},
  isbn      = {978-3-030-13535-5},
  doi       = {10.1007/978-3-030-13535-5_19},
  pages     = {265 -- 276},
  year      = {2019},
  abstract  = {In competition with other modes of transport, rail freight transport is looking for solutions to become more attractive. Short-term success can be achieved through the data-driven optimization of operations and maintenance as well as the application of novel strategies such as prescriptive maintenance. After introducing the concept of prescriptive maintenance, this paper aims to prove that vehicle-focused applications of this approach indeed have the potential to increase attractiveness. However, even greater advantages can be activated if data from the horizontal network of the vehicle is available. Drawing on the state of the art in research and technology in the field of cyber-physical systems (CPS) as well as digital twins and shadows, our work serves to design a system of systems for the horizontal interconnection of a rail vehicle and to conceptualize a draft for a digital twin of a locomotive.},
  language  = {en}
}
@incollection{FateriGebhardt2020,
  author    = {Fateri, Miranda and Gebhardt, Andreas},
  title     = {Introduction to Additive Manufacturing},
  series = {3D Printing of Optical Components},
  booktitle = {3D Printing of Optical Components},
  publisher = {Springer},
  address   = {Cham},
  isbn      = {978-3-030-58960-8},
  doi       = {10.1007/978-3-030-58960-8_1},
  pages     = {1 -- 22},
  year      = {2020},
  abstract  = {Additive manufacturing (AM) works by creating objects layer by layer in a manner similar to a 2D printer with the "printed" layers stacked on top of each other. The layer-wise manufacturing nature of AM enables fabrication of freeform geometries which cannot be fabricated using conventional manufacturing methods as a one part. Depending on how each layer is created and bonded to the adjacent layers, different AM methods have been developed. In this chapter, the basic terms, common materials, and different methods of AM are described, and their potential applications are discussed.},
  language  = {en}
}
@incollection{GebhardtHoetter2019,
  author    = {Gebhardt, Andreas and Hoetter, Jan-Steffen},
  title     = {Rapid Tooling},
  series = {CIRP Encyclopedia of Production Engineering},
  booktitle = {CIRP Encyclopedia of Production Engineering},
  publisher = {Springer},
  address   = {Berlin, Heidelberg},
  isbn      = {978-3-662-53120-4},
  doi       = {10.1007/978-3-662-53120-4},
  pages     = {39 -- 52},
  year      = {2019},
  language  = {en}
}
@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}
}