@misc{10787,
  abstract     = {{Cyber-physical production systems have emerged with the rise of Industry 4.0 in different industrial fields. Especially the food sector, where inhomogeneous input products like beer/yeast suspensions with different qualities and properties have yet slowed down automation, has potential for this evolution. This contribution presents optimization methods for a dynamical cross-flow filtration plant which is driven by an advanced control concept in combination with data driven product monitoring via inline near infrared spectroscopy (NIR) in order to improve energy savings and filtration performance. Using a hierarchical control and optimization structure, the non stationary batch process is steered towards a high production rate with low energy consumption for a variety of different input products.}},
  author       = {{Tebbe, Jörn and Pawlik, Thomas and Trilling-Haasler, Marc and Löbner, Jannis and Lange-Hegermann, Markus and Schneider, Jan}},
  booktitle    = {{2023 IEEE 21st International Conference on Industrial Informatics (INDIN)}},
  editor       = {{Jasperneite, Jürgen and Wisniewski, Lukasz and Fung Man, Kim}},
  isbn         = {{978-1-6654-9314-7 }},
  issn         = {{1935-4576}},
  keywords     = {{Spectroscopy, Production systems, Filtration, Velocity control, Optimization methods, Cyber-physical systems, Nonhomogeneous media}},
  location     = {{Lemgo}},
  pages        = {{1--7}},
  publisher    = {{IEEE}},
  title        = {{{Holistic optimization of a dynamic cross-flow filtration process towards a cyber-physical system}}},
  doi          = {{10.1109/INDIN51400.2023.10217913}},
  year         = {{2023}},
}

@misc{12806,
  abstract     = {{Cyber-Physical Systems (CPS) play an essential role in today’s production processes, leveraging Artificial Intelligence (AI) to enhance operations such as optimization, anomaly detection, and predictive maintenance. This article reviews a cognitive architecture for Artificial Intelligence, which has been developed to establish a standard framework for integrating AI solutions into existing production processes. Given that machines in these processes continuously generate large streams of data, Online Machine Learning (OML) is identified as a crucial extension to the existing architecture. To substantiate this claim, real-world experiments using a slitting machine are conducted, to compare the performance of OML to traditional Batch Machine Learning. The assessment of contemporary OML algorithms using a real production system is a fundamental innovation in this research. The evaluations clearly indicate that OML adds significant value to CPS, and it is strongly recommended as an extension of related architectures, such as the cognitive architecture for AI discussed in this article. Additionally, surrogate-model-based optimization is employed, to determine the optimal hyperparameter settings for the corresponding OML algorithms, aiming to achieve peak performance in their respective tasks.}},
  author       = {{Hinterleitner, Alexander and Schulz, Richard and Hans, Lukas and Subbotin, Aleksandr and Barthel, Nils and Pütz, Noah and Rosellen, Martin and Bartz-Beielstein, Thomas and Geng, Christoph and Priss, Phillip}},
  booktitle    = {{  Applied Sciences : open access journal}},
  issn         = {{2076-3417}},
  keywords     = {{machine learning, online algorithms, cyber-physical production systems, surrogate-based optimization}},
  number       = {{20}},
  publisher    = {{MDPI AG}},
  title        = {{{Online Machine Learning and Surrogate-Model-Based Optimization for Improved Production Processes Using a Cognitive Architecture}}},
  doi          = {{10.3390/app132011506}},
  volume       = {{13}},
  year         = {{2023}},
}

@misc{12790,
  abstract     = {{n the last years, Additive Manufacturing, thanks to its capability of continuous improvements in performance and cost-efficiency, was able to partly replace and redefine well-established manufacturing processes. This research is based on the idea to achieve great cost and operational benefits especially in the field of tool making for injection molding by combining traditional and additive manufacturing in one process chain. Special attention is given to the surface quality in terms of surface roughness and its optimization directly in the Selective Laser Melting process. This article presents the possibility for a remelting process of the SLM parts as a way to optimize the surfaces of the produced parts. The influence of laser remelting on the surface roughness of the parts is analyzed while varying machine parameters like laser power and scan settings. Laser remelting with optimized parameter settings considerably improves the surface quality of SLM parts and is a great starting point for further post-processing techniques, which require a low initial value of surface roughness.}},
  author       = {{Simoni, Filippo and Huxol, Andrea and Villmer, Franz-Josef}},
  booktitle    = {{Journal of Intelligent Manufacturing}},
  issn         = {{1572-8145}},
  keywords     = {{Direct rapid tooling, Toolmaking, Additive manufacturing process chain, Process control, Production systems, Selective laser melting, Surface roughness, Laser surface remelting}},
  number       = {{7}},
  pages        = {{1927--1938}},
  publisher    = {{Springer Science and Business Media}},
  title        = {{{Improving surface quality in selective laser melting based tool making}}},
  doi          = {{10.1007/s10845-021-01744-9}},
  volume       = {{32}},
  year         = {{2021}},
}

@misc{12792,
  abstract     = {{Additive Manufacturing has arisen as a ground-breaking set of technologies that, thanks to their capability of continuous improvements in performance and cost-efficiency, was able in the last years to replace well-established manufacturing processes. Proficiency in the fabrication of highly complex parts forced this astonishing development. This research is based on the idea that through the integration of additive and conventional manufacturing technologies it is possible to achieve great cost and operational benefits especially in the field of tool making for injection molding. Such an integrated manufacturing solution could overcome the limitations of independent additive, subtractive, and post-processing procedures by strengthening their potentialities. The present study highlights the opportunities of a synergy between the above-mentioned manufacturing technologies for the optimized fabrication of injection molds. An additive manufacturing process chain is presented, and special attention is given to the surface quality and its optimization directly in the Selective Laser Melting process. The potentialities of the Laser Surface Re-melting technique are analyzed, and the process optimization leads to a reduction of 45% of the average roughness directly in the SLM process. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.}},
  author       = {{Simoni, Filippo and Huxol, Andrea and Villmer, Franz-Josef}},
  booktitle    = {{13th International-Federation-of-Automatic-Control (IFAC) Workshop on Intelligent Manufacturing Systems (IMS)}},
  issn         = {{2405-8963}},
  keywords     = {{Direct rapid tooling, toolmaking, additive manufacturing process chain, process control, production systems, selective laser melting, surface roughness, laser surface re-melting}},
  location     = {{Oshawa, CANADA}},
  pages        = {{254--259}},
  publisher    = {{Elsevier BV}},
  title        = {{{Approach Towards Surface Improvement in Additively Manufactured Tools}}},
  doi          = {{10.1016/j.ifacol.2019.10.032}},
  volume       = {{52}},
  year         = {{2019}},
}