Abstract
In the era of digitalization, the efficiency of machine tools can be improved due to optimized machining simulations. However, actual simulations only consider the kinematics of the machine tool and not the influence of the machining data, which are changed by experienced machine operators rather frequently. Moreover, these changes of machining data are not always properly communicated. For various reasons, this important knowledge of the machine operators remains tacit. Transferring tacit knowledge of the machine tool operators at the shop floor automatically to a database has not been possible so far. This paper describes a workflow for the acquisition of verified machining data and how to transfer them to a database.
| Originalsprache | englisch |
|---|---|
| Titel | ICITM 2020 - 2020 9th International Conference on Industrial Technology and Management |
| Herausgeber (Verlag) | IEEE |
| Seiten | 117-121 |
| Seitenumfang | 5 |
| ISBN (elektronisch) | 978-1-7281-4306-4 |
| DOIs | |
| Publikationsstatus | Veröffentlicht - 5 Feb. 2020 |
| Veranstaltung | 9th International Conference on Industrial Technology and Management - Oxford, Großbritannien / Vereinigtes Königreich Dauer: 11 Feb. 2020 → 13 Feb. 2020 |
Publikationsreihe
| Name | ICITM 2020 - 2020 9th International Conference on Industrial Technology and Management |
|---|
Konferenz
| Konferenz | 9th International Conference on Industrial Technology and Management |
|---|---|
| Kurztitel | ICITM 2020 |
| Land/Gebiet | Großbritannien / Vereinigtes Königreich |
| Ort | Oxford |
| Zeitraum | 11/02/20 → 13/02/20 |
ASJC Scopus subject areas
- Informationssysteme und -management
- Entscheidungswissenschaften (sonstige)
- Technologie- und Innovationsmanagement
- Wirtschaftsingenieurwesen und Fertigungstechnik
- Strategie und Management
- Managementlehre und Operations Resarch
UN SDGs
Dieser Output leistet einen Beitrag zu folgendem(n) Ziel(en) für nachhaltige Entwicklung
