---
_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.
article_number: '11506'
author:
- first_name: Alexander
  full_name: Hinterleitner, Alexander
  last_name: Hinterleitner
- first_name: Richard
  full_name: Schulz, Richard
  last_name: Schulz
- first_name: Lukas
  full_name: Hans, Lukas
  last_name: Hans
- first_name: Aleksandr
  full_name: Subbotin, Aleksandr
  last_name: Subbotin
- first_name: Nils
  full_name: Barthel, Nils
  last_name: Barthel
- first_name: Noah
  full_name: Pütz, Noah
  last_name: Pütz
- first_name: Martin
  full_name: Rosellen, Martin
  last_name: Rosellen
- first_name: Thomas
  full_name: Bartz-Beielstein, Thomas
  last_name: Bartz-Beielstein
- first_name: Christoph
  full_name: Geng, Christoph
  id: '61408'
  last_name: Geng
- first_name: Phillip
  full_name: Priss, Phillip
  last_name: Priss
citation:
  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>'
  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>'
  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>.'
  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>.'
  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>,
    .'
  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'
  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).'
  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>.'
  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>.'
  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).'
  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.'
  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).'
date_created: 2025-04-16T07:27:52Z
date_updated: 2025-06-26T07:50:56Z
department:
- _id: DEP5023
doi: 10.3390/app132011506
external_id:
  isi:
  - '001096019200001'
intvolume: '        13'
isi: '1'
issue: '20'
keyword:
- machine learning
- online algorithms
- cyber-physical production systems
- surrogate-based optimization
language:
- iso: eng
place: Basel
publication: '  Applied Sciences : open access journal'
publication_identifier:
  issn:
  - 2076-3417
publication_status: published
publisher: MDPI AG
status: public
title: Online Machine Learning and Surrogate-Model-Based Optimization for Improved
  Production Processes Using a Cognitive Architecture
type: scientific_journal_article
user_id: '83781'
volume: 13
year: '2023'
...