Kollaboratives Modelltraining und Datensicherheit Unternehmensübergreifende Verwertung von Werkzeugverschleißdaten für generalisierbare Zustandsüberwachungsmodelle

Daniel Piendl; Andy Ludwig; Jakob Rothe; Michael F. Zäh

doi:10.1515/zwf-2024-0116

Kollaboratives Modelltraining und Datensicherheit Unternehmensübergreifende Verwertung von Werkzeugverschleißdaten für generalisierbare Zustandsüberwachungsmodelle

Translated title of the contribution: Collaborative Model Training and Data Security

Daniel Piendl
, Andy Ludwig
, Jakob Rothe
, Michael F. Zäh

Chair of Machine Tools and Manufacturing Technology

Technical University of Munich
Fraunhofer AISEC
Siemens AG

Research output: Contribution to journal › Article › peer-review

Abstract

Data-based models for tool condition monitoring require a large amount of data that can be generated by individual companies only with high effort. The cross-company collection of data as a possible solution can, however, expose the intellectual property of the companies. To prevent this disclosure, confidentialityprotecting technologies can be employed. The application of these technologies is investigated within the research project MINERVA.

Translated title of the contribution	Collaborative Model Training and Data Security
Original language	German
Pages (from-to)	214-218
Number of pages	5
Journal	ZWF Zeitschrift fuer Wirtschaftlichen Fabrikbetrieb
Volume	120
Issue number	s1
DOIs	https://doi.org/10.1515/zwf-2024-0116
State	Published - 20 Mar 2025

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10.1515/zwf-2024-0116

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title = "Kollaboratives Modelltraining und Datensicherheit Unternehmens{\"u}bergreifende Verwertung von Werkzeugverschlei{\ss}daten f{\"u}r generalisierbare Zustands{\"u}berwachungsmodelle",

abstract = "Data-based models for tool condition monitoring require a large amount of data that can be generated by individual companies only with high effort. The cross-company collection of data as a possible solution can, however, expose the intellectual property of the companies. To prevent this disclosure, confidentialityprotecting technologies can be employed. The application of these technologies is investigated within the research project MINERVA.",

keywords = "Artificial Intelligence, Cloud Computing, Collaboration, Condition Monitoring, Industry 4.0, Machine Tool",

author = "Daniel Piendl and Andy Ludwig and Jakob Rothe and Z{\"a}h, \{Michael F.\}",

note = "Publisher Copyright: {\textcopyright} 2025 Daniel Piendl, Andy Ludwig, Jakob Rothe und Michael F. Z{\"a}h, publiziert von De Gruyter.",

year = "2025",

month = mar,

day = "20",

doi = "10.1515/zwf-2024-0116",

language = "Deutsch",

volume = "120",

pages = "214--218",

journal = "ZWF Zeitschrift fuer Wirtschaftlichen Fabrikbetrieb",

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publisher = "Walter de Gruyter GmbH",

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}

Kollaboratives Modelltraining und Datensicherheit Unternehmensübergreifende Verwertung von Werkzeugverschleißdaten für generalisierbare Zustandsüberwachungsmodelle. / Piendl, Daniel; Ludwig, Andy; Rothe, Jakob et al.
In: ZWF Zeitschrift fuer Wirtschaftlichen Fabrikbetrieb, Vol. 120, No. s1, 20.03.2025, p. 214-218.

Research output: Contribution to journal › Article › peer-review

TY - JOUR

T1 - Kollaboratives Modelltraining und Datensicherheit Unternehmensübergreifende Verwertung von Werkzeugverschleißdaten für generalisierbare Zustandsüberwachungsmodelle

AU - Piendl, Daniel

AU - Ludwig, Andy

AU - Rothe, Jakob

AU - Zäh, Michael F.

PY - 2025/3/20

Y1 - 2025/3/20

N2 - Data-based models for tool condition monitoring require a large amount of data that can be generated by individual companies only with high effort. The cross-company collection of data as a possible solution can, however, expose the intellectual property of the companies. To prevent this disclosure, confidentialityprotecting technologies can be employed. The application of these technologies is investigated within the research project MINERVA.

AB - Data-based models for tool condition monitoring require a large amount of data that can be generated by individual companies only with high effort. The cross-company collection of data as a possible solution can, however, expose the intellectual property of the companies. To prevent this disclosure, confidentialityprotecting technologies can be employed. The application of these technologies is investigated within the research project MINERVA.

KW - Artificial Intelligence

KW - Cloud Computing

KW - Collaboration

KW - Condition Monitoring

KW - Industry 4.0

KW - Machine Tool

UR - https://www.scopus.com/pages/publications/105004556916

U2 - 10.1515/zwf-2024-0116

DO - 10.1515/zwf-2024-0116

M3 - Artikel

AN - SCOPUS:105004556916

SN - 0947-0085

VL - 120

SP - 214

EP - 218

JO - ZWF Zeitschrift fuer Wirtschaftlichen Fabrikbetrieb

JF - ZWF Zeitschrift fuer Wirtschaftlichen Fabrikbetrieb

IS - s1

ER -

Kollaboratives Modelltraining und Datensicherheit Unternehmensübergreifende Verwertung von Werkzeugverschleißdaten für generalisierbare Zustandsüberwachungsmodelle

Abstract

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