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

共 50 条

[21] Microstructure and properties of FeCrMoVC tool steel produced by selective laser melting
Sander, J.
Hufenbach, J.
Giebeler, L.
Wendrock, H.
Kuehn, U.
Eckert, J.
MATERIALS & DESIGN, 2016, 89 : 335 - 341
[22] Machine learning for monitoring and predictive maintenance of cutting tool wear for clean-cut machining machines
Bonci, Andrea
Di Biase, Alessandro
Dragoni, Aldo Franco
Longhi, Sauro
Sernani, Paolo
Zega, Alessandro
2022 IEEE 27TH INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGIES AND FACTORY AUTOMATION (ETFA), 2022,
[23] Machine learning-based optimization of process parameters in selective laser melting for biomedical applications
Hong Seok Park
Dinh Son Nguyen
Thai Le-Hong
Xuan Van Tran
Journal of Intelligent Manufacturing, 2022, 33 : 1843 - 1858
[24] Predictive Maintenance of Machine Tool Linear Axes: A Case from Manufacturing Industry
Schmidt, Bernard
Wang, Lihui
28TH INTERNATIONAL CONFERENCE ON FLEXIBLE AUTOMATION AND INTELLIGENT MANUFACTURING (FAIM2018): GLOBAL INTEGRATION OF INTELLIGENT MANUFACTURING AND SMART INDUSTRY FOR GOOD OF HUMANITY, 2018, 17 : 118 - 125
[25] Machine learning-based optimization of process parameters in selective laser melting for biomedical applications
Park, Hong Seok
Nguyen, Dinh Son
Le-Hong, Thai
Van Tran, Xuan
JOURNAL OF INTELLIGENT MANUFACTURING, 2022, 33 (06) : 1843 - 1858
[26] A hybrid predictive maintenance approach for CNC machine tool driven by Digital Twin
Luo, Weichao
Hu, Tianliang
Ye, Yingxin
Zhang, Chengrui
Wei, Yongli
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING, 2020, 65
[27] Efficient predictive model of part distortion and residual stress in selective laser melting
Li, C.
Liu, J. F.
Fang, X. Y.
Guo, Y. B.
ADDITIVE MANUFACTURING, 2017, 17 : 157 - 168
[28] Research of melting state identification and process performance based on selective laser melting acoustic signals
Chen, Dongju
Wang, Anqing
Wang, Peng
Li, Na
JOURNAL OF LASER APPLICATIONS, 2024, 36 (01)
[29] A scalable predictive model and validation for residual stress and distortion in selective laser melting
Li, Chao
Guo, Yuebin
Fang, Xiaoying
Fang, Fengzhou
CIRP ANNALS-MANUFACTURING TECHNOLOGY, 2018, 67 (01) : 249 - 252
[30] Predictive maintenance based on anomaly detection in photovoltaic system using SCADA data and machine learning
Syamsuddin, Agussalim
Adhi, Andrew Cahyo
Kusumawardhani, Amie
Prahasto, Toni
Widodo, Achmad
Results in Engineering, 2024, 24

← 1 2 3 4 5 →