Ziegenbein, Amina ; Stanula, Patrick ; Metternich, Joachim ; Abele, Eberhard
eds.: Schmitt, Robert ; Schuh, Günther (2018)
Machine Learning Algorithms in Machining: A Guideline for Efficient Algorithm Selection.
In: Advances in Production Research, Proceedings of the 8th Congress of the German Academic Association for Production Technology (WGP)
doi: 10.1007/978-3-030-03451-1₂₉
Book Section
Abstract
The manufacturing industry has difficulties with the question of how advanced analytics, can be integrated into production. This paper describes the algorithm selection step of an overall methodology for the systematic implementation of data mining projects in production. This is intended to provide users with a guideline to what a basic procedure may look like and what steps should be considered. First, this procedure is explained, which is then performed and illustrated on an application of high-frequency machine data.
| Item Type: | Book Section |
|---|---|
| Erschienen: | 2018 |
| Editors: | Schmitt, Robert ; Schuh, Günther |
| Creators: | Ziegenbein, Amina ; Stanula, Patrick ; Metternich, Joachim ; Abele, Eberhard |
| Type of entry: | Bibliographie |
| Title: | Machine Learning Algorithms in Machining: A Guideline for Efficient Algorithm Selection |
| Language: | English |
| Date: | November 2018 |
| Publisher: | Springer Cham |
| Book Title: | Advances in Production Research, Proceedings of the 8th Congress of the German Academic Association for Production Technology (WGP) |
| DOI: | 10.1007/978-3-030-03451-1₂₉ |
| URL / URN: | https://link.springer.com/chapter/10.1007/978-3-030-03451-1_... |
| Abstract: | The manufacturing industry has difficulties with the question of how advanced analytics, can be integrated into production. This paper describes the algorithm selection step of an overall methodology for the systematic implementation of data mining projects in production. This is intended to provide users with a guideline to what a basic procedure may look like and what steps should be considered. First, this procedure is explained, which is then performed and illustrated on an application of high-frequency machine data. |
| Uncontrolled Keywords: | Machine tool, Predictive model, Quality assurance |
| Divisions: | 16 Department of Mechanical Engineering 16 Department of Mechanical Engineering > Institute of Production Technology and Machine Tools (PTW) 16 Department of Mechanical Engineering > Institute of Production Technology and Machine Tools (PTW) > Management of Industrial Production |
| Date Deposited: | 24 Apr 2019 09:33 |
| Last Modified: | 24 Apr 2019 09:33 |
| PPN: | |
| Export: | |
| Suche nach Titel in: | TUfind oder in Google |
 |
Send an inquiry |
Options (only for editors)
 |
Show editorial Details |
Drucken
Impressum