期刊
12TH CIRP CONFERENCE ON INTELLIGENT COMPUTATION IN MANUFACTURING ENGINEERING
卷 79, 期 -, 页码 403-408出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.procir.2019.02.106
关键词
Data mining; Machine learning; Manufacturing data management; Data driven process optimisation
资金
- European Social Fund (ESF) [100302264]
- German Federal State of Saxony
The value of data analytics is fundamental in cyber-physical production systems for tasks like optimization and predictive maintenance. The de facto standard for conducting data analytics in industrial applications is the CRISP-DM methodology. However, CRISP-DM does not specify a data acquisition phase within production scenarios. With this work, we present DMME as an extension to the CRISP-DM methodology specifically tailored for engineering applications. It provides a communication and planning foundation for data analytics within the production domain. We show the feasibility of our methodology for engineering applications within a case study in the field of work piece detection. (C) 2019 The Authors. Published by Elsevier B.V.
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