Hybrid Machine Learning – When Little Data is Available

Fuerst, Axel; Walther, Simon (20 May 2024). Hybrid Machine Learning – When Little Data is Available In: 10th Annual World Congress of Advanced Materials WCAM-2024 Japan. Osaka, Japan. May.21-24 2024.

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The prediction of wear conditions in production are worth hard cash. In highly productive plants, which are common today, even smaller extensions of the production intervals lead to considerable cost reductions. With the machine learning tools that are available today, it is relatively easy to develop predictions. These tools are partly even free, but especially for the deep learning algorithms a lot of data is needed. However, are usually not enough data sets available, because the processes are so efficient that only a low wear is present and certain wear patterns occur only rarely. This is where Hybrid Machine Learning comes in. Since in engineering certain relations can be described well by physical clothing, but usually the boundary conditions and certain operating parameters are not known exactly, one can develop models that are based partly on physical descriptions partly on machine learning.

Item Type:	Conference or Workshop Item (Speech)
Division/Institute:	School of Engineering and Computer Science > Forschung und Dienstleistungen (FDLT) School of Engineering and Computer Science > Intelligente industrielle Systeme (I3S) School of Engineering and Computer Science > Intelligente industrielle Systeme (I3S) > I3S / Prozessoptimierung in der Fertigung School of Engineering and Computer Science
Name:	Fuerst, Axel and Walther, Simonhttps://orcid.org/0000-0001-9839-2054
Subjects:	Q Science > Q Science (General) Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General)
Language:	English
Submitter:	Simon Walther
Date Deposited:	07 Aug 2024 08:42
Last Modified:	07 Aug 2024 08:42
ARBOR DOI:	10.24451/arbor.22054
URI:	https://arbor.bfh.ch/id/eprint/22054