Computer numerical control CNC machine health prediction using multi-domain feature extraction and deep neural network regression
Joint Authors
Ibrahim, Dina Adil
al-Barwani, Muhammad A.
al-Munir, Hamdi K.
al-Attar, Hatim M.
Abd al-Hamid, Ibrahim
Source
Journal of Engineering Research
Issue
Vol. 6, Issue 5 (31 Dec. 2022), pp.7-12, 6 p.
Publisher
Tanta University Faculty of Engineering
Publication Date
2022-12-31
Country of Publication
Egypt
No. of Pages
6
Main Subjects
Electronic engineering
Information Technology and Computer Science
Topics
Abstract EN
Tool wear monitoring has become more vital in intelligent production to enhance computer numerical control CNC machine health state.
multi domain features may effectively define tool wear status and help tool wear prediction.
prognostics and health management (PHM) plays a vital role in condition-based maintenance (CBM) to prevent rather than detect malfunctions in machinery.
this has great advantage of saving costs of fault repair including human effort, financial costs as long as power and energy consumption.
the huge evolution of industrial internet of things (IIOT) and industrial big data analytics has made deep learning a growing field of research.
the PHM society has held many competitions including PHM10 concerning CNC milling machine cutters data for tool wear prediction the purpose of this paper is to predict tool wear of CNC cutters and.
we adopted a multi-domain feature extraction method for health statement of the cutters.
and a deep neural network DNN method for tool wear prediction.
American Psychological Association (APA)
Ibrahim, Dina Adil& al-Barwani, Muhammad A.& al-Munir, Hamdi K.& al-Attar, Hatim M.& Abd al-Hamid, Ibrahim. 2022. Computer numerical control CNC machine health prediction using multi-domain feature extraction and deep neural network regression. Journal of Engineering Research،Vol. 6, no. 5, pp.7-12.
https://search.emarefa.net/detail/BIM-1454538
Modern Language Association (MLA)
Ibrahim, Dina Adil…[et al.]. Computer numerical control CNC machine health prediction using multi-domain feature extraction and deep neural network regression. Journal of Engineering Research Vol. 6, no. 5 (Dec. 2022), pp.7-12.
https://search.emarefa.net/detail/BIM-1454538
American Medical Association (AMA)
Ibrahim, Dina Adil& al-Barwani, Muhammad A.& al-Munir, Hamdi K.& al-Attar, Hatim M.& Abd al-Hamid, Ibrahim. Computer numerical control CNC machine health prediction using multi-domain feature extraction and deep neural network regression. Journal of Engineering Research. 2022. Vol. 6, no. 5, pp.7-12.
https://search.emarefa.net/detail/BIM-1454538
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 11-12
Record ID
BIM-1454538