Automated continuous learn and improvement process of energy efficiency in manufacturing

被引：0

作者：

Can, Alperen ^{[1
]}

Fisch, Jessica ^{[1
]}

Stephan, Philipp ^{[1
]}

Thiele, Gregor ^{[2
]}

Krueger, Joerg ^{[3
]}

机构：

[1] Mercedes Benz AG, D-12277 Berlin, Germany

[2] Fraunhofer Inst Prod Syst & Design Technol IPK, Pascalstr 8-9, D-10587 Berlin, Germany

[3] Tech Univ Berlin, Ind Automat Technol Grp, Pascalstr 8-9, D-10587 Berlin, Germany

来源：

IECON 2020: THE 46TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY | 2020年

关键词：

automated optimization; data analytics; process optimization; continuous improvement process; energy efficiency;

D O I：

10.1109/iecon43393.2020.9255088

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Optimizing the energy efficiency of machine tools automatically is promising. There are several metrics to be considered when it comes to automated optimization approaches in serial production which are especially quality, technical availability, and cycle time. These are not supposed to be impaired whereas they are indicated as a central obstacle. The measurements and the machine data show the actions happening in the machine which also leads to the data-driven traceability of machine states. This article presents a method to formulate the necessary expert knowledge to optimize the energy efficiency of a machine tool and is basically done by a decision tree which leads to a set of rules which will be explained in this article. This set of rules coordinate an optimization algorithm, which technically manipulates selected variables under the given rules. The development and is a result of a research which was done at the serial production of camshafts at the MB plant in Berlin.

引用

页码：757 / 762

页数：6