[{"external_id":{"isi":["001096019200001"]},"date_created":"2025-04-16T07:27:52Z","language":[{"iso":"eng"}],"year":"2023","date_updated":"2025-06-26T07:50:56Z","article_number":"11506","user_id":"83781","volume":13,"type":"scientific_journal_article","_id":"12806","abstract":[{"lang":"eng","text":"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."}],"place":"Basel","status":"public","author":[{"full_name":"Hinterleitner, Alexander","last_name":"Hinterleitner","first_name":"Alexander"},{"first_name":"Richard","last_name":"Schulz","full_name":"Schulz, Richard"},{"first_name":"Lukas","full_name":"Hans, Lukas","last_name":"Hans"},{"first_name":"Aleksandr","last_name":"Subbotin","full_name":"Subbotin, Aleksandr"},{"first_name":"Nils","last_name":"Barthel","full_name":"Barthel, Nils"},{"last_name":"Pütz","full_name":"Pütz, Noah","first_name":"Noah"},{"first_name":"Martin","last_name":"Rosellen","full_name":"Rosellen, Martin"},{"first_name":"Thomas","full_name":"Bartz-Beielstein, Thomas","last_name":"Bartz-Beielstein"},{"first_name":"Christoph","last_name":"Geng","id":"61408","full_name":"Geng, Christoph"},{"first_name":"Phillip","full_name":"Priss, Phillip","last_name":"Priss"}],"publication":"  Applied Sciences : open access journal","isi":"1","issue":"20","doi":"10.3390/app132011506","title":"Online Machine Learning and Surrogate-Model-Based Optimization for Improved Production Processes Using a Cognitive Architecture","department":[{"_id":"DEP5023"}],"publication_status":"published","publication_identifier":{"issn":["2076-3417"]},"keyword":["machine learning","online algorithms","cyber-physical production systems","surrogate-based optimization"],"publisher":"MDPI AG","citation":{"din1505-2-1":"<span style=\"font-variant:small-caps;\"><span style=\"font-variant:small-caps;\">Hinterleitner, Alexander</span> ; <span style=\"font-variant:small-caps;\">Schulz, Richard</span> ; <span style=\"font-variant:small-caps;\">Hans, Lukas</span> ; <span style=\"font-variant:small-caps;\">Subbotin, Aleksandr</span> ; <span style=\"font-variant:small-caps;\">Barthel, Nils</span> ; <span style=\"font-variant:small-caps;\">Pütz, Noah</span> ; <span style=\"font-variant:small-caps;\">Rosellen, Martin</span> ; <span style=\"font-variant:small-caps;\">Bartz-Beielstein, Thomas</span> ; u. a.</span>: Online Machine Learning and Surrogate-Model-Based Optimization for Improved Production Processes Using a Cognitive Architecture. In: <i>  Applied Sciences : open access journal</i> Bd. 13. Basel, MDPI AG (2023), Nr. 20","short":"A. Hinterleitner, R. Schulz, L. Hans, A. Subbotin, N. Barthel, N. Pütz, M. Rosellen, T. Bartz-Beielstein, C. Geng, P. Priss,   Applied Sciences : Open Access Journal 13 (2023).","chicago":"Hinterleitner, Alexander, Richard Schulz, Lukas Hans, Aleksandr Subbotin, Nils Barthel, Noah Pütz, Martin Rosellen, Thomas Bartz-Beielstein, Christoph Geng, and Phillip Priss. “Online Machine Learning and Surrogate-Model-Based Optimization for Improved Production Processes Using a Cognitive Architecture.” <i>  Applied Sciences : Open Access Journal</i> 13, no. 20 (2023). <a href=\"https://doi.org/10.3390/app132011506\">https://doi.org/10.3390/app132011506</a>.","mla":"Hinterleitner, Alexander, et al. “Online Machine Learning and Surrogate-Model-Based Optimization for Improved Production Processes Using a Cognitive Architecture.” <i>  Applied Sciences : Open Access Journal</i>, vol. 13, no. 20, 11506, 2023, <a href=\"https://doi.org/10.3390/app132011506\">https://doi.org/10.3390/app132011506</a>.","chicago-de":"Hinterleitner, Alexander, Richard Schulz, Lukas Hans, Aleksandr Subbotin, Nils Barthel, Noah Pütz, Martin Rosellen, Thomas Bartz-Beielstein, Christoph Geng und Phillip Priss. 2023. Online Machine Learning and Surrogate-Model-Based Optimization for Improved Production Processes Using a Cognitive Architecture. <i>  Applied Sciences : open access journal</i> 13, Nr. 20. doi:<a href=\"https://doi.org/10.3390/app132011506\">10.3390/app132011506</a>, .","bjps":"<b>Hinterleitner A <i>et al.</i></b> (2023) Online Machine Learning and Surrogate-Model-Based Optimization for Improved Production Processes Using a Cognitive Architecture. <i>  Applied Sciences : open access journal</i> <b>13</b>.","ufg":"<b>Hinterleitner, Alexander u. a.</b>: Online Machine Learning and Surrogate-Model-Based Optimization for Improved Production Processes Using a Cognitive Architecture, in: <i>  Applied Sciences : open access journal</i> 13 (2023), H. 20.","havard":"A. Hinterleitner, R. Schulz, L. Hans, A. Subbotin, N. Barthel, N. Pütz, M. Rosellen, T. Bartz-Beielstein, C. Geng, P. Priss, Online Machine Learning and Surrogate-Model-Based Optimization for Improved Production Processes Using a Cognitive Architecture,   Applied Sciences : Open Access Journal. 13 (2023).","apa":"Hinterleitner, A., Schulz, R., Hans, L., Subbotin, A., Barthel, N., Pütz, N., Rosellen, M., Bartz-Beielstein, T., Geng, C., &#38; Priss, P. (2023). Online Machine Learning and Surrogate-Model-Based Optimization for Improved Production Processes Using a Cognitive Architecture. <i>  Applied Sciences : Open Access Journal</i>, <i>13</i>(20), Article 11506. <a href=\"https://doi.org/10.3390/app132011506\">https://doi.org/10.3390/app132011506</a>","ama":"Hinterleitner A, Schulz R, Hans L, et al. Online Machine Learning and Surrogate-Model-Based Optimization for Improved Production Processes Using a Cognitive Architecture. <i>  Applied Sciences : open access journal</i>. 2023;13(20). doi:<a href=\"https://doi.org/10.3390/app132011506\">10.3390/app132011506</a>","van":"Hinterleitner A, Schulz R, Hans L, Subbotin A, Barthel N, Pütz N, et al. Online Machine Learning and Surrogate-Model-Based Optimization for Improved Production Processes Using a Cognitive Architecture.   Applied Sciences : open access journal. 2023;13(20).","ieee":"A. Hinterleitner <i>et al.</i>, “Online Machine Learning and Surrogate-Model-Based Optimization for Improved Production Processes Using a Cognitive Architecture,” <i>  Applied Sciences : open access journal</i>, vol. 13, no. 20, Art. no. 11506, 2023, doi: <a href=\"https://doi.org/10.3390/app132011506\">10.3390/app132011506</a>."},"intvolume":"        13"},{"publication":"KommA 2020 – Jahreskolloquium Kommunikation in der Automation","conference":{"end_date":"2020-10-29","start_date":"2020-10-29","location":"Lemgo","name":"11. Jahreskolloquium KommA 2020"},"date_created":"2021-01-26T12:04:18Z","title":"Concept for Rule-Based Information Aggregation in Modular Production Plants","department":[{"_id":"DEP5023"}],"language":[{"iso":"eng"}],"year":2020,"date_updated":"2023-03-15T13:49:53Z","user_id":"68554","_id":"4512","type":"conference","citation":{"havard":"C. Geng, N. Moriz, A. Bunte, H. Trsek, Concept for Rule-Based Information Aggregation in Modular Production Plants, in: KommA 2020 – Jahreskolloquium Kommunikation in Der Automation, Lemgo, Germany, 2020.","ufg":"<b>Geng, Christoph et. al. (2020)</b>: Concept for Rule-Based Information Aggregation in Modular Production Plants, in: <i>KommA 2020 – Jahreskolloquium Kommunikation in der Automation</i>, Lemgo, Germany.","ama":"Geng C, Moriz N, Bunte A, Trsek H. Concept for Rule-Based Information Aggregation in Modular Production Plants. In: <i>KommA 2020 – Jahreskolloquium Kommunikation in Der Automation</i>. Lemgo, Germany; 2020.","apa":"Geng, C., Moriz, N., Bunte, A., &#38; Trsek, H. (2020). Concept for Rule-Based Information Aggregation in Modular Production Plants. In <i>KommA 2020 – Jahreskolloquium Kommunikation in der Automation</i>. Lemgo, Germany.","ieee":"C. Geng, N. Moriz, A. Bunte, and H. Trsek, “Concept for Rule-Based Information Aggregation in Modular Production Plants,” in <i>KommA 2020 – Jahreskolloquium Kommunikation in der Automation</i>, Lemgo, 2020.","van":"Geng C, Moriz N, Bunte A, Trsek H. Concept for Rule-Based Information Aggregation in Modular Production Plants. In: KommA 2020 – Jahreskolloquium Kommunikation in der Automation. Lemgo, Germany; 2020.","mla":"Geng, Christoph, et al. “Concept for Rule-Based Information Aggregation in Modular Production Plants.” <i>KommA 2020 – Jahreskolloquium Kommunikation in Der Automation</i>, 2020.","short":"C. Geng, N. Moriz, A. Bunte, H. Trsek, in: KommA 2020 – Jahreskolloquium Kommunikation in Der Automation, Lemgo, Germany, 2020.","chicago":"Geng, Christoph, Natalia Moriz, Andreas Bunte, and Henning Trsek. “Concept for Rule-Based Information Aggregation in Modular Production Plants.” In <i>KommA 2020 – Jahreskolloquium Kommunikation in Der Automation</i>. Lemgo, Germany, 2020.","din1505-2-1":"<span style=\"font-variant:small-caps;\">Geng, Christoph</span> ; <span style=\"font-variant:small-caps;\">Moriz, Natalia</span> ; <span style=\"font-variant:small-caps;\">Bunte, Andreas</span> ; <span style=\"font-variant:small-caps;\">Trsek, Henning</span>: Concept for Rule-Based Information Aggregation in Modular Production Plants. In: <i>KommA 2020 – Jahreskolloquium Kommunikation in der Automation</i>. Lemgo, Germany, 2020","bjps":"<b>Geng C <i>et al.</i></b> (2020) Concept for Rule-Based Information Aggregation in Modular Production Plants. <i>KommA 2020 – Jahreskolloquium Kommunikation in Der Automation</i>. Lemgo, Germany.","chicago-de":"Geng, Christoph, Natalia Moriz, Andreas Bunte und Henning Trsek. 2020. Concept for Rule-Based Information Aggregation in Modular Production Plants. In: <i>KommA 2020 – Jahreskolloquium Kommunikation in der Automation</i>. Lemgo, Germany."},"place":"Lemgo, Germany","author":[{"last_name":"Geng","full_name":"Geng, Christoph","id":"61408","first_name":"Christoph"},{"first_name":"Natalia","id":"44238","full_name":"Moriz, Natalia","last_name":"Moriz"},{"first_name":"Andreas","last_name":"Bunte","id":"58885","full_name":"Bunte, Andreas"},{"first_name":"Henning","full_name":"Trsek, Henning","id":"1486","last_name":"Trsek"}],"status":"public"}]