Cluster identification of sensor data for predictive maintenance in a Selective Laser Melting machine tool

被引：35

作者：

Uhlmann, Eckart ^{[1
,2
]}

Pontes, Rodrigo Pastl ^{[1
]}

Geisert, Claudio ^{[1
]}

Hohwieler, Eckhard ^{[1
]}

机构：

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

[2] Tech Univ Berlin, Inst Machine Tools & Factory Management IWF, Pascalstr 8-9, D-10587 Berlin, Germany

来源：

4TH INTERNATIONAL CONFERENCE ON SYSTEM-INTEGRATED INTELLIGENCE: INTELLIGENT, FLEXIBLE AND CONNECTED SYSTEMS IN PRODUCTS AND PRODUCTION | 2018年 / 24卷

关键词：

Selective laser melting; sensor data; machine learning; clustering; predictive maintenance;

D O I：

10.1016/j.promfg.2018.06.009

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Selective laser melting has become one of the most current new technologies used to produce complex components in comparison to conventional manufacturing technologies. Especially, existing selective laser melting machine tools are not equipped with analytics tools that evaluate sensor data. This paper describes an approach to analyze and visualize offline data from different sources based on machine learning algorithms. Data from three sensors were utilized to identify clusters. They illustrate the normal operation of the machine tool and three faulty conditions. With these results, a condition monitoring system can be implemented that enables those machine tools for predictive maintenance solutions. (C) 2018 The Authors. Published by Elsevier B.V.

引用

页码：60 / 65

页数：6

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