Liwen Hu , Baihui Chen , ElHussein Shata , Shashank Shekhar , Charif Mahmoudi , Ivan Seskar , Qingze Zou , Y.B. Guo
{"title":"Feasibility of 5G-enabled process monitoring in milling operations","authors":"Liwen Hu , Baihui Chen , ElHussein Shata , Shashank Shekhar , Charif Mahmoudi , Ivan Seskar , Qingze Zou , Y.B. Guo","doi":"10.1016/j.mfglet.2024.09.024","DOIUrl":null,"url":null,"abstract":"<div><div>5G monitoring holds immense potential for revolutionizing manufacturing processes by enabling real-time data transmission, remote control, enhanced quality control, and increased efficiency. However, it also presents challenges related to 5G monitoring infrastructure. To explore 5G’s potential for process monitoring, this study introduces a novel 5G-enabled architecture designed to address the challenges, enhancing the process monitoring’s efficiency, accuracy, and reliability in the case of milling operation. To investigate the feasibility of this sophisticated 5G network for process monitoring, two testbeds, i.e., the 5G robotic milling testbed and the 5G CNC milling testbed, have been developed. An accelerometer and a laser scanner have been retrofitted with 5G communications capability to capture critical process signals in the testbeds, respectively. It has shown that the sensor data can be upstreamed to a 5G edge server for data analytics and visualization in ultra-low latency. This work highlights the transformative impact of 5G communication on process monitoring for time-critical manufacturing.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"41 ","pages":"Pages 200-207"},"PeriodicalIF":1.9000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Manufacturing Letters","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2213846324000816","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, MANUFACTURING","Score":null,"Total":0}
引用次数: 0
Abstract
5G monitoring holds immense potential for revolutionizing manufacturing processes by enabling real-time data transmission, remote control, enhanced quality control, and increased efficiency. However, it also presents challenges related to 5G monitoring infrastructure. To explore 5G’s potential for process monitoring, this study introduces a novel 5G-enabled architecture designed to address the challenges, enhancing the process monitoring’s efficiency, accuracy, and reliability in the case of milling operation. To investigate the feasibility of this sophisticated 5G network for process monitoring, two testbeds, i.e., the 5G robotic milling testbed and the 5G CNC milling testbed, have been developed. An accelerometer and a laser scanner have been retrofitted with 5G communications capability to capture critical process signals in the testbeds, respectively. It has shown that the sensor data can be upstreamed to a 5G edge server for data analytics and visualization in ultra-low latency. This work highlights the transformative impact of 5G communication on process monitoring for time-critical manufacturing.