@inproceedings{AdenackerGerhardsOttenetal.2021,
  author    = {Adenacker, J. and Gerhards, Benjamin and Otten, Christian and Schleser, Markus},
  title     = {Laserstrahlschweißen von Aluminium-Kupfer-Werkstoffkombinationen f{\"u}r die Elektromobilit{\"a}t},
  series = {DVS CONGRESS 2021},
  booktitle = {DVS CONGRESS 2021},
  publisher = {DVS Media GmbH},
  address   = {D{\"u}sseldorf},
  isbn      = {978-3-96144-146-4},
  pages     = {31 -- 38},
  year      = {2021},
  language  = {de}
}
@article{ZabirovSchleserBucherer2021,
  author    = {Zabirov, Alexander and Schleser, Markus and Bucherer, Sebastian},
  title     = {F{\"u}ge- und Dichtkonzept f{\"u}r einen Leichtbauverbrennungsmotor},
  series = {adh{\"a}sion KLEBEN \& DICHTEN},
  volume    = {65},
  journal   = {adh{\"a}sion KLEBEN \& DICHTEN},
  number    = {11},
  publisher = {Springer Nature},
  address   = {Cham},
  issn      = {2192-8681},
  doi       = {10.1007/s35145-021-0531-5},
  pages     = {12 -- 19},
  year      = {2021},
  language  = {de}
}
@article{KaschSchmidtJahnetal.2021,
  author    = {Kasch, Susanne and Schmidt, Thomas and Jahn, Simon and Eichler, Fabian and Thurn, Laura and Bremen, Sebastian},
  title     = {L{\"o}sungsans{\"a}tze und Verfahrenskonzepte zum Laserstrahlschmelzen von Glas},
  series = {Schweissen und Schneiden},
  volume    = {73},
  journal   = {Schweissen und Schneiden},
  number    = {Heft 1-2},
  publisher = {DVS Verlag},
  address   = {D{\"u}sseldorf},
  isbn      = {0036-7184},
  pages     = {32 -- 39},
  year      = {2021},
  language  = {de}
}
@inproceedings{PfeifferBalcGebhardt2021,
  author    = {Pfeiffer, Johann and Balc, Nicolae and Gebhardt, Andreas},
  title     = {Studie zur Untersuchung der Auswirkung von Fr{\"a}sbahnstrategien auf die Oberfl{\"a}chenqualit{\"a}t von mittels SLM gefertigten Metallteilen},
  series = {Tagungsband 21. Nachwuchswissenschaftler*innenkonferenz},
  booktitle = {Tagungsband 21. Nachwuchswissenschaftler*innenkonferenz},
  publisher = {Verlag Ernst-Abbe-Hochschule Jena},
  address   = {Jena},
  isbn      = {978-3-932886-36-2},
  pages     = {99 -- 102},
  year      = {2021},
  abstract  = {F{\"u}r die Herstellung von metallischen Bauteilen wird in der heutigen Zeit eine Vielzahl von Verfahren auf dem Markt angeboten. Dabei stehen die additiven im Wettbewerb zu den konventionellen Verfahren. Die erreichbaren Oberfl{\"a}chenqualit{\"a}ten der additiven sind nicht mit denen spanender Verfahren vergleichbar. F{\"u}r diesen Beitrag wurde analysiert, ob sich ein mittels Selektivem Laserschmelzen (SLM) additiv hergestellter Edelstahl hinsichtlich seiner Oberfl{\"a}chenqualit{\"a}t nach der Zerspanung von einem umgeformten konventionell hergestellten Edelstahl gleicher Sorte unterscheidet.},
  language  = {de}
}
@inproceedings{UlmerBraunChengetal.2021,
  author    = {Ulmer, Jessica and Braun, Sebastian and Cheng, Chi-Tsun and Dowey, Steve and Wollert, J{\"o}rg F.},
  title     = {Adapting augmented reality systems to the users' needs using gamification and error solving methods},
  series = {Procedia CIRP - 54th CIRP CMS 2021 - Towards Digitalized Manufacturing 4.0},
  volume    = {104},
  booktitle = {Procedia CIRP - 54th CIRP CMS 2021 - Towards Digitalized Manufacturing 4.0},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {2212-8271},
  doi       = {10.1016/j.procir.2021.11.024},
  pages     = {140 -- 145},
  year      = {2021},
  abstract  = {Animations of virtual items in AR support systems are typically predefined and lack interactions with dynamic physical environments. AR applications rarely consider users' preferences and do not provide customized spontaneous support under unknown situations. This research focuses on developing adaptive, error-tolerant AR systems based on directed acyclic graphs and error resolving strategies. Using this approach, users will have more freedom of choice during AR supported work, which leads to more efficient workflows. Error correction methods based on CAD models and predefined process data create individual support possibilities. The framework is implemented in the Industry 4.0 model factory at FH Aachen.},
  language  = {en}
}
@article{BraunChengDoweyetal.2021,
  author    = {Braun, Sebastian and Cheng, Chi-Tsun and Dowey, Steve and Wollert, J{\"o}rg F.},
  title     = {Performance evaluation of skill-based order-assignment in production environments with multi-agent systems},
  series = {IEEE Journal of Emerging and Selected Topics in Industrial Electronics},
  journal   = {IEEE Journal of Emerging and Selected Topics in Industrial Electronics},
  number    = {Early Access},
  publisher = {IEEE},
  address   = {New York},
  issn      = {2687-9735},
  doi       = {10.1109/JESTIE.2021.3108524},
  year      = {2021},
  abstract  = {The fourth industrial revolution introduces disruptive technologies to production environments. One of these technologies are multi-agent systems (MASs), where agents virtualize machines. However, the agent's actual performances in production environments can hardly be estimated as most research has been focusing on isolated projects and specific scenarios. We address this gap by implementing a highly connected and configurable reference model with quantifiable key performance indicators (KPIs) for production scheduling and routing in single-piece workflows. Furthermore, we propose an algorithm to optimize the search of extrema in highly connected distributed systems. The benefits, limits, and drawbacks of MASs and their performances are evaluated extensively by event-based simulations against the introduced model, which acts as a benchmark. Even though the performance of the proposed MAS is, on average, slightly lower than the reference system, the increased flexibility allows it to find new solutions and deliver improved factory-planning outcomes. Our MAS shows an emerging behavior by using flexible production techniques to correct errors and compensate for bottlenecks. This increased flexibility offers substantial improvement potential. The general model in this paper allows the transfer of the results to estimate real systems or other models.},
  language  = {en}
}
@inproceedings{UlmerBraunWollert2021,
  author    = {Ulmer, Jessica and Braun, Sebastian and Wollert, J{\"o}rg F.},
  title     = {Adaptive VR-Produktionsumgebungen f{\"u}r Evaluations- und Schulungst{\"a}tigkeiten},
  series = {Automation 2021: Navigating towards resilient Production},
  booktitle = {Automation 2021: Navigating towards resilient Production},
  publisher = {VDI},
  address   = {D{\"u}sseldorf},
  isbn      = {978-3-18-092392-5},
  issn      = {0083-5560},
  doi       = {10.51202/9783181023921-55},
  pages     = {55 -- 64},
  year      = {2021},
  abstract  = {Industrie 4.0 stellt viele Herausforderungen an produzierende Unternehmen und ihre Besch{\"a}f-tigten. Innovative und effektive Trainingsstrategien sind erforderlich, um mit den sich schnell ver{\"a}ndernden Produktionsumgebungen und neuen Fertigungstechnologien Schritt halten zu k{\"o}nnen. Virtual Reality (VR) bietet neue M{\"o}glichkeiten f{\"u}r On-the-Job, On-Demand- und Off-Premise-Schulungen. Diese Arbeit stellt ein neues VR Schulungssystem vor, welches sich flexible an unterschiedliche Trainingsobjekte auf Grundlage von Rezepten und CAD Modellen anpassen l{\"a}sst. Das Konzept basiert auf gerichteten azyklischen Graphen und einem Level-system. Es erm{\"o}glicht eine benutzerindividuelle Lerngeschwindigkeit mittels visueller Ele-mente. Das Konzept wurde f{\"u}r einen mechanischen Anwendungsfall mit Industriekomponen-ten implementiert und in der Industrie 4.0-Modellfabrik der FH Aachen umgesetzt.},
  language  = {de}
}
@article{AbbasBalcBremenetal.2022,
  author    = {Abbas, Karim and Balc, Nicolae and Bremen, Sebastian and Skupin, Marco},
  title     = {Crystallization and aging behavior of polyetheretherketone PEEK within rapid tooling and rubber molding},
  series = {Journal of Manufacturing and Materials Processing},
  volume    = {6},
  journal   = {Journal of Manufacturing and Materials Processing},
  number    = {5},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {2504-4494},
  doi       = {10.3390/jmmp6050093},
  pages     = {1 -- 12},
  year      = {2022},
  abstract  = {In times of short product life cycles, additive manufacturing and rapid tooling are important methods to make tool development and manufacturing more efficient. High-performance polymers are the key to mold production for prototypes and small series. However, the high temperatures during vulcanization injection molding cause thermal aging and can impair service life. The extent to which the thermal stress over the entire process chain stresses the material and whether it leads to irreversible material aging is evaluated. To this end, a mold made of PEEK is fabricated using fused filament fabrication and examined for its potential application. The mold is heated to 200 ◦C, filled with rubber, and cured. A differential scanning calorimetry analysis of each process step illustrates the crystallization behavior and first indicates the material resistance. It shows distinct cold crystallization regions at a build chamber temperature of 90 ◦C. At an ambient temperature above Tg, crystallization of 30\% is achieved, and cold crystallization no longer occurs. Additional tensile tests show a decrease in tensile strength after ten days of thermal aging. The steady decrease in recrystallization temperature indicates degradation of the additives. However, the tensile tests reveal steady embrittlement of the material due to increasing crosslinking.},
  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 Leading manufacturing systems transformation - Proceedings of the 55th CIRP Conference on Manufacturing Systems 2022},
  volume    = {107},
  booktitle = {Procedia CIRP Leading manufacturing systems transformation - Proceedings of the 55th CIRP Conference on Manufacturing Systems 2022},
  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}
}
@inproceedings{WeissHeslenfeldSaeweetal.2022,
  author    = {Weiss, Christian and Heslenfeld, Jonas and Saewe, Jasmin Kathrin and Bremen, Sebastian and H{\"a}fner, Constantin Leon},
  title     = {Investigation on the influence of powder humidity in Laser Powder Bed Fusion (LPBF)},
  series = {Procedia CIRP 12th CIRP Conference on Photonic Technologies [LANE 2022]},
  volume    = {111},
  booktitle = {Procedia CIRP 12th CIRP Conference on Photonic Technologies [LANE 2022]},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {2212-8271},
  doi       = {10.1016/j.procir.2022.08.102},
  pages     = {115 -- 120},
  year      = {2022},
  abstract  = {In the Laser Powder Bed Fusion (LPBF) process, parts are built out of metal powder material by exposure of a laser beam. During handling operations of the powder material, several influencing factors can affect the properties of the powder material and therefore directly influence the processability during manufacturing. Contamination by moisture due to handling operations is one of the most critical aspects of powder quality. In order to investigate the influences of powder humidity on LPBF processing, four materials (AlSi10Mg, Ti6Al4V, 316L and IN718) are chosen for this study. The powder material is artificially humidified, subsequently characterized, manufactured into cubic samples in a miniaturized process chamber and analyzed for their relative density. The results indicate that the processability and reproducibility of parts made of AlSi10Mg and Ti6Al4V are susceptible to humidity, while IN718 and 316L are barely influenced.},
  language  = {en}
}