Sensor Networks for Smart Manufacturing Processes

József Konyha; Tamás Bányai

doi:10.4028/www.scientific.net/SSP.261.456

Paper Titles

Recent Developments in Desktop-Sized Machine Tools
p.425

Advanced Metrology and Intelligent Quality Automation for Industry 4.0-Based Precision Manufacturing Systems
p.432

Internet of Things and Industrial Applications for Precision Machining
p.440

On Systematic CAD/CAM Modeling of Blow Molds for Plastic Bottles
p.448

Sensor Networks for Smart Manufacturing Processes
p.456

A Generic Multi-Axis Post-Processor Engine for Optimal CNC Data Creation and Intelligent Surface Machining
p.463

An Ontology Based Semantic Machine Tool Selection for Multi Scale Wire EDM Processes
p.470

New Challenges for Quality Assurance of Manufacturing Processes in Industry 4.0
p.481

Indirect Impacts of Drastic Scrap Rate Reduction on Costs of Production Process in Precision Machining
p.487

HomeSolid State PhenomenaSolid State Phenomena Vol. 261Sensor Networks for Smart Manufacturing Processes

Sensor Networks for Smart Manufacturing Processes

Abstract:

Each factory and manufacturing plant needs a flexible and reliable in-plant resource supply to serve production processes efficiently. Manufacturing systems are composed of several numbers of elements, workstations, machines and logistics resources. Production line is a complex system because of the complicated manufacturing process, multiple types, high machining difficulty and many special processes in it. In the Industry 4.0 based on smart manufacturing, it is essential to support the processes with intelligent sensor networks. In this article, we give a brief overview about sensors often used in manufacturing processes. Sensor networks generate a massive and increasing amount of data that needs to be processed. Computationally intensive algorithms are used for the data processing (image, voice and signal processing, different classification functions, numeric optimization routines). Finally, we discuss how GPGPU can improve the real-time processing of data generated by intelligent sensor networks.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Solid State Phenomena (Volume 261)

Pages:

456-462

DOI:

https://doi.org/10.4028/www.scientific.net/SSP.261.456

Citation:

Cite this paper

Online since:

August 2017

Authors:

József Konyha, Tamás Bányai*

Keywords:

GPGPU, Intelligent Sensor Networks, Parallel Processing, Smart Manufacturing

Export:

RIS, BibTeX

Price:

Permissions:

Request Permissions

* - Corresponding Author

References

[1] J. Korponai, A. Banyaine Toth, B. Illes, The effect of the safety stock on the occurrence probability of the stock shortage, Manag. Prod. Eng. Rev. 8(1) (2017) 69-77.

DOI: 10.1515/mper-2017-0008

Google Scholar

[2] E. A. Lee, Cyber physical systems: Design challenges, Proc. Obj. Oriented RT. Dist. Comp. (2008) IEEE 363–369.

Google Scholar

[3] W. Wolf, Cyber physical systems, Computer 42(3) (2009) 88-89.

Google Scholar

[4] J. Fraden, Handbook of Modern Sensors - Physics, Designs, and Applications, fifth ed. Switzerland, (2016).

Google Scholar

[5] S. Mekid, Further Structural Intelligence for Sensors Cluster Technology in Manufacturing, Sensors 6(6) (2006) 557-577.

DOI: 10.3390/s6060557

Google Scholar

[6] I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci, Wireless sensor networks: a survey, Comput. Networks 38(4) (2002) 393-422.

DOI: 10.1016/s1389-1286(01)00302-4

Google Scholar

[7] T. L. Johnson, M. E. Dausch, Sensor Informatics for Manufacturing, P. IFAC Proc. Vol. 39, (2006) 125-130.

Google Scholar

[8] Y. Hao, P. Helo, The role of wearable devices in meeting the needs of cloud manufacturing: A case study, Robot. CIM-Int. Manuf. 45 (2017) 168-179.

DOI: 10.1016/j.rcim.2015.10.001

Google Scholar

[9] Theorin, K. Bengtsson, J. Provost, M. Lieder, T. Johnsson, T. Lundholm, B. Lennartson, An event-driven manufacturing information system architecture for Industry 4. 0, Int. J. Prod. Res. 55(5) (2017) 1297-1311.

DOI: 10.1080/00207543.2016.1201604

Google Scholar

[10] J. Konyha, T. Banyai, Approach to accelerate algorithms to solve logistic problems with GPGPU, J. Adv. Log. Sys. 10 (2016) 5-10.

Google Scholar

[11] V. Fuvesi, J. Konyha, Review of machine learning toolboxes, P. M. Tud. EM (2016) 116-122.

Google Scholar

[12] F. Lopez, L. Zhang, A Mok, J Beaman, Particle filtering on GPU architectures for manufacturing applications, Comp. in Industry 71 (2015) 116–127.

DOI: 10.1016/j.compind.2015.03.013

Google Scholar

[13] D. Wayland, R. Montemanni, L. M. Gambardella, A metaheuristic framework for stochastic combinatorial optimization problems based on GPGPU with a case study on the probabilistic traveling salesman problem with deadlines, J. Parallel Distrib. Comput. 73 (2013).

DOI: 10.1016/j.jpdc.2012.05.004

Google Scholar

[14] I. M. Coelho, V. M. Coelho, E. J. S. Luz, L. S. Ochi, F. G. Guimarães, E. Rios, A GPU deep learning metaheuristic based model for time series forecasting, Applied Energy (2017) In P.

DOI: 10.1016/j.apenergy.2017.01.003

Google Scholar