Sage Journals: Discover world-class research

Abstract

The safe, stable, efficient, and environmentally friendly operation of laser cutting machines is crucial for laser cutting companies to enhance productivity and ensure safe production. Real-time acquisition, analysis, and diagnosis of machine tool system operation status information provide technical support for machine tool fault diagnosis and maintenance, thereby achieving optimal operational efficiency. The advancements in edge computing and the growing application of artificial intelligence have rendered traditional laser cutting machine tool monitoring and assistance systems obsolete in the context of modern intelligent manufacturing. This paper presents a remote monitoring and assistance system for laser cutting machine tools based on cloud edge collaboration. Intelligent devices are utilised for on-site data collection, employing various communication protocols, including HTTP and WebSocket. It realises comprehensive control of the IoT platform, equipment diagnosis and data analysis on the remote expert maintenance platform, and autonomous decision-making at the edge. It also forms the corresponding web-based interface and PC and mobile monitoring and interaction software. Laboratory tests have demonstrated the system’s viability in enhancing the productivity and maintenance of laser cutting machines. It provides a cost-effective remote diagnostic and maintenance solution for laser processing companies.

Keywords

laser cutting cloud edge collaboration auxiliary systems edge computing

Get full access to this article

View all access options for this article.

References

Kan

Yang

Kumara

. Parallel computing and network analytics for fast Industrial Internet-of-Things (IIoT) machine information processing and condition monitoring. J Manuf Syst 2018; 46: 282–293.

Liu

. Cyber-physical machine tool–the era of machine tool 4.0. Procedia Cirp 2017; 63: 70–75.

Ferdows

De Meyer

. Lasting improvements in manufacturing performance: in search of a new theory. J of Ops Management 1990; 9(2): 168–184.

Liu

Peng

, et al. MTConnect-based cyber-physical machine tool: a case study. Procedia Cirp 2018; 72: 492–497.

Zhu

Zhang

. A cyber-physical production system framework of smart CNC machining monitoring system. IEEE ASME Trans Mechatron 2018; 23(6): 2579–2586.

Lin

Y-L

Lin

C-C

Chiu

H-S

(eds). The development of intelligent service system for machine tool industry. In: 2015 1st International Conference on Industrial Networks and Intelligent Systems (Iniscom), Tokyo, Japan, 2–4 March 2015, IEEE, 2015.

Caggiano

Segreto

Teti

. Cloud manufacturing framework for smart monitoring of machining. Procedia Cirp 2016; 55: 248–253.

Shi

Zhang

Wang

, et al. Edge computing: current status and prospects. J Comput Res Dev 2019; 56(01): 69–89.

Shi

Cao

Zhang

, et al. Edge computing: vision and challenges. IEEE Internet Things J 2016; 3(5): 637–646.

10.

Zhi Zhou

Chen

. Edge intelligence: a new nexus of edge computing and artificial intelligence. Big Data Research 2019; 5(2): 53–63.

11.

Hung

M-H

Lin

Y-C

Huy

, et al. (eds). Development of a cloud-computing-based equipment monitoring system for machine tool industry. In: 2012 IEEE International Conference on Automation Science and Engineering (CASE), Seoul, South Korea, 20–24 August 2012, IEEE, 2012.

12.

Bhinge

Biswas

Dornfeld

, et al. (eds). An intelligent machine monitoring system for energy prediction using a Gaussian Process regression. In: 2014 IEEE International Conference on Big Data (Big Data), Washington, DC, USA, 27–30 October 2014, IEEE, 2014.

13.

Ding

Zhang

Yang

. Machine tool fault classification diagnosis based on audio parameters. Results in Engineering 2023; 19: 101308.

14.

Zhang

Zhou

, et al. A multi-access edge computing enabled framework for the construction of a knowledge-sharing intelligent machine tool swarm in Industry 4.0. J Manuf Syst 2023; 66: 56–70.

15.

Iuhasz

Dragan

(eds). An overview of monitoring tools for big data and cloud applications. In: 2015 17th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), Timisoara, Romania, 21–24 September 2015, IEEE, 2015.

16.

Tongxing

Xiaolong

Lejian

, et al. Development of remote intelligent fault diagnosis system based on network. Mach Tool & Hydra 2017; 45(19): 199–204.

17.

Libing

Man

, et al. Research on fault diagnosis methods of CNC machine tool during the operation based on multi source information fusion. Manufacturing Technology & Machine Tool 2016.

18.

Huang

Zhao

. Application of cloud computing in intelligent machine tool control system. J Mech Eng 2018; 54(8): 210–216.

19.

Cao

Liu

Meng

, et al. An overview on edge computing research. IEEE Access 2020; 8: 85714–85728.

20.

Kong

Wang

, et al. Edge computing for internet of everything: a survey. IEEE Internet Things J 2022; 9(23): 23472–23485.

21.

Wang

. Kubeedge. AI: AI platform for edge devices. arXiv preprint arXiv:200709227 2020.

22.

Yang

Ning

Lan

, et al. Kubeedge wireless for integrated communication and computing services everywhere. IEEE Wireless Commun 2022; 29(2): 140–145.

Laser cutting machine assistance system based on cloud edge collaboration

Abstract

Keywords

Get full access to this article

References