Online Machine Learning and Surrogate-Model-Based Optimization for Improved Production Processes Using a Cognitive Architecture
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Published:2023-10-20
Issue:20
Volume:13
Page:11506
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ISSN:2076-3417
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Container-title:Applied Sciences
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language:en
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Short-container-title:Applied Sciences
Author:
Hinterleitner Alexander1ORCID, Schulz Richard1ORCID, Hans Lukas1ORCID, Subbotin Aleksandr1ORCID, Barthel Nils1, Pütz Noah1, Rosellen Martin1, Bartz-Beielstein Thomas1ORCID, Geng Christoph2, Priss Phillip2
Affiliation:
1. Institute for Data Science, Engineering and Analytics, TH Köln University of Applied Sciences, 51643 Gummersbach, Germany 2. Institute for Industrial Information Technology—inIT, OWL University of Applied Sciences and Arts, 32657 Lemgo, Germany
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.
Funder
German Federal Ministry for Economic Affairs and Climate Action
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference21 articles.
1. Adolphs, P., Bedenbender, H., Dirzus, D., Ehlich, M., Epple, U., Hankel, M., Heidel, R., Hoffmeister, M., Huhle, H., and Kaercher, B. (2015). Reference Architecture Model Industrie 4.0 (RAMI4.0), VDI. Tech. Rep. 2. Lin, S.W., Miller, B., Durand, J., Bleakley, G., Ghigani, A., Martin, R., Murphy, B., and Crawford, M. (2017). The Industrial Internet of Things Volume G1: Reference Architecture v1.80, Industrial Internet Consortium. Technical Report. 3. Cyber physical systems for predictive production systems;Lee;Prod. Eng.,2017 4. SOAR: An Architecture for General Intelligence;Laird;Artif. Intell.,1987 5. A Simple Theory of Complex Cognition;Anderson;Am. Psychol.,1996
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