@article{LuftLuftArntz2023,
  author    = {Luft, Angela and Luft, Nils and Arntz, Kristian},
  title     = {A basic description logic for service-oriented architecture in factory planning and operational control in the age of industry 4.0},
  series = {Applied Sciences},
  volume    = {2023},
  journal   = {Applied Sciences},
  number    = {13},
  publisher = {MDPI},
  address   = {Basel},
  doi       = {10.3390/app13137610},
  pages     = {Artikel 7610},
  year      = {2023},
  abstract  = {Manufacturing companies across multiple industries face an increasingly dynamic and unpredictable environment. This development can be seen on both the market and supply side. To respond to these challenges, manufacturing companies must implement smart manufacturing systems and become more flexible and agile. The flexibility in operational planning regarding the scheduling and sequencing of customer orders needs to be increased and new structures must be implemented in manufacturing systems' fundamental design as they constitute much of the operational flexibility available. To this end, smart and more flexible solutions for production planning and control (PPC) are developed. However, scheduling or sequencing is often only considered isolated in a predefined stable environment. Moreover, their orientation on the fundamental logic of the existing IT solutions and their applicability in a dynamic environment is limited. This paper presents a conceptual model for a task-based description logic that can be applied to factory planning, technology planning, and operational control. By using service-oriented architectures, the goal is to generate smart manufacturing systems. The logic is designed to allow for easy and automated maintenance. It is compatible with the existing resource and process allocation logic across operational and strategic factory and production planning.},
  language  = {en}
}
@inproceedings{DannenSchindelePruemmeretal.2022,
  author    = {Dannen, Tammo and Schindele, Benedikt and Pr{\"u}mmer, Marcel and Arntz, Kristian and Bergs, Thomas},
  title     = {Methodology for the self-optimizing determination of additive manufacturing process eligibility and optimization potentials in toolmaking},
  series = {Procedia CIRP},
  volume    = {107},
  booktitle = {Procedia CIRP},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {2212-8271},
  doi       = {10.1016/j.procir.2022.05.188},
  pages     = {1539 -- 1544},
  year      = {2022},
  abstract  = {Additive Manufacturing (AM) of metallic workpieces faces a continuously rising technological relevance and market size. Producing complex or highly strained unique workpieces is a significant field of application, making AM highly relevant for tool components. Its successful economic application requires systematic workpiece based decisions and optimizations. Considering geometric and technological requirements as well as the necessary post-processing makes deciding effortful and requires in-depth knowledge. As design is usually adjusted to established manufacturing, associated technological and strategic potentials are often neglected. To embed AM in a future proof industrial environment, software-based self-learning tools are necessary. Integrated into production planning, they enable companies to unlock the potentials of AM efficiently. This paper presents an appropriate methodology for the analysis of process-specific AM-eligibility and optimization potential, added up by concrete optimization proposals. For an integrated workpiece characterization, proven methods are enlarged by tooling-specific figures. The first stage of the approach specifies the model's initialization. A learning set of tooling components is described using the developed key figure system. Based on this, a set of applicable rules for workpiece-specific result determination is generated through clustering and expert evaluation. Within the following application stage, strategic orientation is quantified and workpieces of interest are described using the developed key figures. Subsequently, the retrieved information is used for automatically generating specific recommendations relying on the generated ruleset of stage one. Finally, actual experiences regarding the recommendations are gathered within stage three. Statistic learning transfers those to the generated ruleset leading to a continuously deepening knowledge base. This process enables a steady improvement in output quality.},
  language  = {en}
}
@article{WollbrinkMasloZimmeretal.2020,
  author    = {Wollbrink, Moritz and Maslo, Semir and Zimmer, Daniel and Abbas, Karim and Arntz, Kristian and Bergs, Thomas},
  title     = {Clamping and substrate plate system for continuous additive build-up and post-processing of metal parts},
  series = {Procedia CIRP},
  volume    = {93},
  journal   = {Procedia CIRP},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {2212-8271},
  doi       = {10.1016/j.procir.2020.04.015},
  pages     = {108 -- 113},
  year      = {2020},
  abstract  = {The manufacturing share of laser powder bed fusion (L-PBF) increases in industrial application, but still many process steps are manually operated. Additionally, it is not possible to achieve tight dimensional tolerances or low surfaces roughness. Hence, a process chain has to be set up to combine additive manufacturing (AM) with further machining technologies. To achieve a continuous workpiece flow as basis for further industrialization of L-PBF, the paper presents a novel substrate system and its application on L-PBF machines and post-processing. The substrate system consists of a zero-point clamping system and a matrix-like interface of contact pins to be substantially connected to the workpiece within the L-PBF process.},
  language  = {en}
}