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Machine Learning Algorithms in Machining: A Guideline for Efficient Algorithm Selection

Ziegenbein, Amina and Stanula, Patrick and Metternich, Joachim and Abele, Eberhard
Schmitt, Robert and Schuh, Günther (eds.) (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), Springer Cham, pp. 288-299, DOI: 10.1007/978-3-030-03451-1₂₉,
[Online-Edition: https://link.springer.com/chapter/10.1007/978-3-030-03451-1_...],
[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 and Schuh, Günther
Creators: Ziegenbein, Amina and Stanula, Patrick and Metternich, Joachim and Abele, Eberhard
Title: Machine Learning Algorithms in Machining: A Guideline for Efficient Algorithm Selection
Language: English
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.

Title of Book: Advances in Production Research, Proceedings of the 8th Congress of the German Academic Association for Production Technology (WGP)
Publisher: Springer Cham
ISBN: 978-3-030-03451-1
Uncontrolled Keywords: Machine tool, Predictive model, Quality assurance
Divisions: 16 Department of Mechanical Engineering
16 Department of Mechanical Engineering > Institute of Production Management, Technology and Machine Tools (PTW)
16 Department of Mechanical Engineering > Institute of Production Management, Technology and Machine Tools (PTW) > Management of Industrial Production
Date Deposited: 24 Apr 2019 09:33
DOI: 10.1007/978-3-030-03451-1₂₉
Official URL: https://link.springer.com/chapter/10.1007/978-3-030-03451-1_...
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