Abstract
Fast and accurate data channelling between Machine-to-Human (M2H) and Machineto- Machine (M2M) are necessary in Smart Manufacturing implementation. In this paper, a data channelling architecture, i.e., Open Platform Communication Unified Architecture (OPC UA) is used to test on manufacturing machine. Data will be channelled and stored into a centralized hub through OPC UA. Predictive analysis process, one of the useful features of Smart Manufacturing is used to analyse the data stored in centralized hub instantly when the data is collected from the machine. This instantaneous action is to make sure the data in the data collector, which is from the machine will not alter or missing. In this research, OPC UA standard is applied to ensure the results of data channelling fulfill the requirement of Smart Manufacturing implementation. As a result, the OPC UA Server serves more data channeling processes when there is an increment of data bits and it reduces the transmission speed, in both local and VPN connection.
Keywords: Data channelling, OPC UA, Industry 4.0, real-time data, Smart Manufacturing
Authors
Ng Kheng Hui [1] ; Tew Yiqi [2] ; Yip Mun Wai [3]
[1][2] Faculty of Computing and Information Technology, Tunku Abdul Rahman University of Management and Technology, Kuala Lumpur, Malaysia
[3] Faculty of Engineering and Technology, Tunku Abdul Rahman University of Management and Technology, Kuala Lumpur, Malaysia
[1] ngkh-wt12@student.tarc.edu.my, [2] yiqi@tarc.edu.my, [3] yipmw@tarc.edu.my
Cite Me
Plain Text:
K.H.Ng, Y.Tew, M.W.Yip, "Exploration on Machine Sensor Data Channelling Towards Smart Manufacturing Implementation," International Conference on Digital Transformation and Applications (ICDXA) 2020, 2020, pp. 151-157, doi: https://doi.org/10.56453/icdxa.2020.1018.
BibTex:
@INPROCEEDINGS{ICDXA2020T304,
author={Ng, Kheng Hui and Tew, Yiqi and Yip, Mun Wai},
booktitle={International Conference on Digital Transformation and Applications (ICDXA) 2020},
title={Exploration on Machine Sensor Data Channelling Towards Smart Manufacturing Implementation},
year={2020},
volume={},
number={},
pages={151-157},
doi={https://doi.org/10.56453/icdxa.2020.1018}}
