Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Online Machine Learning and Surrogate Model-Based Optimization for Improved Production Processes Using a Cognitive Architecture

Alexander Hinterleitner
ORCID logo ,
Richard Schulz
,
Lukas Hans
,
Aleksandr Subbotin
ORCID logo ,
Nils Barthel
,
Noah Pütz
,
Martin Rosellen
,
Thomas Bartz-Beielstein
* ORCID logo ,
Christoph Geng
,
Phillip Priss
Version 1 : Received: 4 October 2023 / Approved: 5 October 2023 / Online: 7 October 2023 (04:57:38 CEST)

A peer-reviewed article of this Preprint also exists.

Hinterleitner, A.; Schulz, R.; Hans, L.; Subbotin, A.; Barthel, N.; Pütz, N.; Rosellen, M.; Bartz-Beielstein, T.; Geng, C.; Priss, P. Online Machine Learning and Surrogate-Model-Based Optimization for Improved Production Processes Using a Cognitive Architecture. Appl. Sci. 2023, 13, 11506. Hinterleitner, A.; Schulz, R.; Hans, L.; Subbotin, A.; Barthel, N.; Pütz, N.; Rosellen, M.; Bartz-Beielstein, T.; Geng, C.; Priss, P. Online Machine Learning and Surrogate-Model-Based Optimization for Improved Production Processes Using a Cognitive Architecture. Appl. Sci. 2023, 13, 11506.

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) was identified as a crucial extension to the existing architecture. To substantiate this claim, real-world experiments using a slitting machine were conducted to compare the performance of OML with traditional Batch Machine Learning. The evaluations clearly indicate that OML adds significant value to CPS and is strongly recommeded 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.

Keywords

machine learning; online algorithms; cyber-physical production systems; surrogate based optimization

Subject

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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