Temat: machining 4.0 - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Integration of OPC UA information models into Enterprise Knowledge Graphs
Autorzy:: Weiss, Arno
Ihlenfeldt, Steffen
Powiązania:: https://bibliotekanauki.pl/articles/2086280.pdf
Data publikacji:: 2022
Wydawca:: Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych
Tematy:: digital twin
manufacturing system
machining 4.0
machine tool
intelligent function
Opis:: Building repositories of data relevant for enterprise operations requires harmonization of formats and semantics. OPC UA’s nodes-and-references data model shares basic elements with well-established semantic modeling technologies like RDF. This paper suggests the use of transformed OPC UA information models on the higher level of Enterprise Knowledge Graphs. It proposes good practice to integrate the separate domains by representing OPC UA servers as RDF-graphs and subsequently attaching them to Digital Twins embedded in Enterprise Knowledge Graph structures. The developed practice is implemented, applied to combine a server’s structure with an existing knowledge graph containing an Asset Administration Shell and released open source.
Źródło:: Journal of Machine Engineering; 2022, 22, 2; 138--147
1895-7595
2391-8071
Pojawia się w:: Journal of Machine Engineering
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Smart tool-related faults monitoring system using process simulation-based machine learning algorithms
Autorzy:: Ebrahimi Araghizad, Arash
Tehranizadeh, Faraz
Kilic, Kemal
Budak, Erhan
Powiązania:: https://bibliotekanauki.pl/articles/28407322.pdf
Data publikacji:: 2023
Wydawca:: Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych
Tematy:: Industry 4.0
machining
machine learning
monitoring
Opis:: In this paper a novel approach for monitoring tool-related faults in milling processes by utilizing process simulation-based machine learning algorithms, specifically Random Forest algorithms, for fault detection is presented. In order to train machine learning models in tool condition monitoring, laboratory tests have traditionally been required. This method eliminates the need for costly, time-consuming laboratory tests. The training process has been simplified by utilizing analytical simulation data and provides a more cost-effective solution by leveraging analytical simulation data. Based on the results of this study, the proposed approach has been demonstrated to be 94% accurate at predicting tool-related faults, demonstrating its potential to serve as an efficient and viable alternative to conventional methods. These findings have been supported by actual measurement data, with a notable accuracy rate of 93% in the predictions. Furthermore, the results indicate that process simulation-based machine learning algorithms will have a significant impact on the tools condition monitoring and the efficiency of manufacturing processes more generally. To further enhance the capabilities of the proposed fault monitoring system, process-related and machine-related faults will be investigated in future research. Several machine learning algorithms will be explored as well as additional data sources will be integrated in order to enhance the accuracy and reliability of fault detection.
Źródło:: Journal of Machine Engineering; 2023, 23, 4; 18--32
1895-7595
2391-8071
Pojawia się w:: Journal of Machine Engineering
Dostawca treści:: Biblioteka Nauki

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