中文版 | English
Title

An Intelligent Manufacturing Approach Based on a Novel Deep Learning Method for Automatic Machine and Working Status Recognition

Author
Corresponding AuthorAhmad, Rafiq
Publication Years
2022-06-01
DOI
Source Title
EISSN
2076-3417
Volume12Issue:11
Abstract
Smart manufacturing uses robots and artificial intelligence techniques to minimize human interventions in manufacturing activities. Inspection of the machine' working status is critical in manufacturing processes, ensuring that machines work correctly without any collisions and interruptions, e.g., in lights-out manufacturing. However, the current method heavily relies on workers onsite or remotely through the Internet. The existing approaches also include a hard-wired robot working with a computer numerical control (CNC) machine, and the instructions are followed through a pre-program path. Currently, there is no autonomous machine tending application that can detect and act upon the operational status of a CNC machine. This study proposes a deep learning-based method for the CNC machine detection and working status recognition through an independent robot system without human intervention. It is noted that there is often more than one machine working in a representative industrial environment. Therefore, the SiameseRPN method is developed to recognize and locate a specific machine from a group of machines. A deep learning-based text recognition method is designed to identify the working status from the human-machine interface (HMI) display.
Keywords
URL[Source Record]
Indexed By
Language
English
SUSTech Authorship
Others
Funding Project
NSERC["ALLRP561048-20","CRDPJ 537378-18"]
WOS Research Area
Chemistry ; Engineering ; Materials Science ; Physics
WOS Subject
Chemistry, Multidisciplinary ; Engineering, Multidisciplinary ; Materials Science, Multidisciplinary ; Physics, Applied
WOS Accession No
WOS:000809189400001
Publisher
Data Source
Web of Science
Citation statistics
Cited Times [WOS]:1
Document TypeJournal Article
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/343076
DepartmentDepartment of Mechanical and Energy Engineering
Affiliation
1.Univ Alberta, Dept Mech Engn, Lab Intelligent Mfg Design & Automat LIMDA, Edmonton, AB T6G 2R3, Canada
2.Southern Univ Sci & Technol, Dept Mech & Energy Engn, Shenzhen 518055, Peoples R China
Recommended Citation
GB/T 7714
Jia, Feiyu,Jebelli, Ali,Ma, Yongsheng,et al. An Intelligent Manufacturing Approach Based on a Novel Deep Learning Method for Automatic Machine and Working Status Recognition[J]. APPLIED SCIENCES-BASEL,2022,12(11).
APA
Jia, Feiyu,Jebelli, Ali,Ma, Yongsheng,&Ahmad, Rafiq.(2022).An Intelligent Manufacturing Approach Based on a Novel Deep Learning Method for Automatic Machine and Working Status Recognition.APPLIED SCIENCES-BASEL,12(11).
MLA
Jia, Feiyu,et al."An Intelligent Manufacturing Approach Based on a Novel Deep Learning Method for Automatic Machine and Working Status Recognition".APPLIED SCIENCES-BASEL 12.11(2022).
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