Computer Numerical Control CNC Machine Health Prediction using Multi-domain Feature Extraction and Deep Neural Network Regression
Tool wear monitoring has become more vital in intelligent production to enhance Computer Numerical Control CNC machine health state. Multidomain features may effectively define tool wear status and help tool wear prediction. Prognostics and health management (PHM) plays a vital role in condition-bas...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | Arabic |
| Published: |
Faculty of engineering, Tanta University
2022-12-01
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| Series: | Journal of Engineering Research - Egypt |
| Subjects: | |
| Online Access: | https://erjeng.journals.ekb.eg/article_262113_2c9fd9243f5b7e6cddce67307b889ffc.pdf |
| _version_ | 1797799156182417408 |
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| author | Dina adel Mohamed El-Barawany Hamdy Elminir Hatem Elattar Ebrahim Abdel Hamid |
| author_facet | Dina adel Mohamed El-Barawany Hamdy Elminir Hatem Elattar Ebrahim Abdel Hamid |
| author_sort | Dina adel |
| collection | DOAJ |
| description | Tool wear monitoring has become more vital in intelligent production to enhance Computer Numerical Control CNC machine health state. Multidomain 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. |
| first_indexed | 2024-03-13T04:14:47Z |
| format | Article |
| id | doaj.art-98f776dc83f6468c9aa93322e60c4328 |
| institution | Directory Open Access Journal |
| issn | 2356-9441 2735-4873 |
| language | Arabic |
| last_indexed | 2024-03-13T04:14:47Z |
| publishDate | 2022-12-01 |
| publisher | Faculty of engineering, Tanta University |
| record_format | Article |
| series | Journal of Engineering Research - Egypt |
| spelling | doaj.art-98f776dc83f6468c9aa93322e60c43282023-06-21T06:44:34ZaraFaculty of engineering, Tanta UniversityJournal of Engineering Research - Egypt2356-94412735-48732022-12-016571210.21608/erjeng.2022.160627.1100262113Computer Numerical Control CNC Machine Health Prediction using Multi-domain Feature Extraction and Deep Neural Network RegressionDina adel0Mohamed El-Barawany1Hamdy Elminir2Hatem Elattar3Ebrahim Abdel Hamid4faculty of engineering, kafrelshiekh universityIndustrial Electronic and Control Engineering Faculty of Electronic Engineering, Menoufia University EgyptDepartment of Electrical Engineering, Faculty of Engineering, Kafr Elshiekh University EgyptInformation System Department, Faculty of Computers and Information Sciences Mansoura University, Mansoura, Egypt.Industrial Electronic and Control Engineering, Faculty of Electronic Engineering Menoufia University EgyptTool wear monitoring has become more vital in intelligent production to enhance Computer Numerical Control CNC machine health state. Multidomain 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.https://erjeng.journals.ekb.eg/article_262113_2c9fd9243f5b7e6cddce67307b889ffc.pdfcnc machineprognostics and health managementcondition based maintenancednn |
| spellingShingle | Dina adel Mohamed El-Barawany Hamdy Elminir Hatem Elattar Ebrahim Abdel Hamid Computer Numerical Control CNC Machine Health Prediction using Multi-domain Feature Extraction and Deep Neural Network Regression Journal of Engineering Research - Egypt cnc machine prognostics and health management condition based maintenance dnn |
| title | Computer Numerical Control CNC Machine Health Prediction using Multi-domain Feature Extraction and Deep Neural Network Regression |
| title_full | Computer Numerical Control CNC Machine Health Prediction using Multi-domain Feature Extraction and Deep Neural Network Regression |
| title_fullStr | Computer Numerical Control CNC Machine Health Prediction using Multi-domain Feature Extraction and Deep Neural Network Regression |
| title_full_unstemmed | Computer Numerical Control CNC Machine Health Prediction using Multi-domain Feature Extraction and Deep Neural Network Regression |
| title_short | Computer Numerical Control CNC Machine Health Prediction using Multi-domain Feature Extraction and Deep Neural Network Regression |
| title_sort | computer numerical control cnc machine health prediction using multi domain feature extraction and deep neural network regression |
| topic | cnc machine prognostics and health management condition based maintenance dnn |
| url | https://erjeng.journals.ekb.eg/article_262113_2c9fd9243f5b7e6cddce67307b889ffc.pdf |
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