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Tool wear prediction in turning using workpiece surface profile images and deep learning neural networks

Accurate prediction of tool flank wear during turning is important so that the cutting tool can be replaced before excessive damage occurs to the workpiece surface. Existing online methods of tool wear prediction using sensor signals can be affected by noise, thus resulting in false alarms. The aim...

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Main Authors: Lim, Meng Lip, Mohd Naqib, Derani, Ratnam, Mani Maran, Ahmad Razlan, Yusoff
Format: Article
Language:English
English
Published: Springer Science and Business Media Deutschland GmbH 2022
Subjects:
T Technology (General)
TA Engineering (General). Civil engineering (General)
TJ Mechanical engineering and machinery
TK Electrical engineering. Electronics Nuclear engineering
TS Manufactures
Online Access:http://umpir.ump.edu.my/id/eprint/42653/1/Tool%20wear%20prediction%20in%20turning%20using%20workpiece%20surface.pdf
http://umpir.ump.edu.my/id/eprint/42653/2/Tool%20wear%20prediction%20in%20turning%20using%20workpiece%20surface%20profile%20images%20and%20deep%20learning%20neural%20networks_ABS.pdf
http://umpir.ump.edu.my/id/eprint/42653/
https://doi.org/10.1007/s00170-022-09257-2
https://doi.org/10.1007/s00170-022-09257-2
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spelling my.ump.umpir.426532025-01-07T03:42:19Z http://umpir.ump.edu.my/id/eprint/42653/ Tool wear prediction in turning using workpiece surface profile images and deep learning neural networks Lim, Meng Lip Mohd Naqib, Derani Ratnam, Mani Maran Ahmad Razlan, Yusoff T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering TS Manufactures Accurate prediction of tool flank wear during turning is important so that the cutting tool can be replaced before excessive damage occurs to the workpiece surface. Existing online methods of tool wear prediction using sensor signals can be affected by noise, thus resulting in false alarms. The aim of this work is to develop deep learning regression models to predict tool wear state using features extracted from 2-D images of surface profile of the workpiece. Two models, namely convolutional neural network (CNN) and deep neural network (DNN), were compared in terms of prediction accuracy. Images of the workpiece surface profile were captured using high-resolution camera with the aid of backlighting after each machining pass. Workpiece surface profile images along a distance of two wavelengths were cropped and fed into the CNN network for wear prediction. For the DNN model, the surface height data were extracted to subpixel accuracy from each cropped image and used to train the model. Based on the results, the CNN model was able to predict the wear state with an accuracy of 98.9%, with an average testing RMSE of 2.0969, while the DNN model can predict wear state at an accuracy of 89.1%, with average testing RMSE of 2.5881. The study shows that cropped images of the machined surface profile can be more reliably used to predict the amount of tool flank wear during turning by using the CNN model compared to the height data used in the DNN model. Springer Science and Business Media Deutschland GmbH 2022-06 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/42653/1/Tool%20wear%20prediction%20in%20turning%20using%20workpiece%20surface.pdf pdf en http://umpir.ump.edu.my/id/eprint/42653/2/Tool%20wear%20prediction%20in%20turning%20using%20workpiece%20surface%20profile%20images%20and%20deep%20learning%20neural%20networks_ABS.pdf Lim, Meng Lip and Mohd Naqib, Derani and Ratnam, Mani Maran and Ahmad Razlan, Yusoff (2022) Tool wear prediction in turning using workpiece surface profile images and deep learning neural networks. International Journal of Advanced Manufacturing Technology, 120 (11-12). pp. 8045-8062. ISSN 0268-3768. (Published) https://doi.org/10.1007/s00170-022-09257-2 https://doi.org/10.1007/s00170-022-09257-2
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
English
topic T Technology (General)
TA Engineering (General). Civil engineering (General)
TJ Mechanical engineering and machinery
TK Electrical engineering. Electronics Nuclear engineering
TS Manufactures
spellingShingle T Technology (General)
TA Engineering (General). Civil engineering (General)
TJ Mechanical engineering and machinery
TK Electrical engineering. Electronics Nuclear engineering
TS Manufactures
Lim, Meng Lip
Mohd Naqib, Derani
Ratnam, Mani Maran
Ahmad Razlan, Yusoff
Tool wear prediction in turning using workpiece surface profile images and deep learning neural networks
description Accurate prediction of tool flank wear during turning is important so that the cutting tool can be replaced before excessive damage occurs to the workpiece surface. Existing online methods of tool wear prediction using sensor signals can be affected by noise, thus resulting in false alarms. The aim of this work is to develop deep learning regression models to predict tool wear state using features extracted from 2-D images of surface profile of the workpiece. Two models, namely convolutional neural network (CNN) and deep neural network (DNN), were compared in terms of prediction accuracy. Images of the workpiece surface profile were captured using high-resolution camera with the aid of backlighting after each machining pass. Workpiece surface profile images along a distance of two wavelengths were cropped and fed into the CNN network for wear prediction. For the DNN model, the surface height data were extracted to subpixel accuracy from each cropped image and used to train the model. Based on the results, the CNN model was able to predict the wear state with an accuracy of 98.9%, with an average testing RMSE of 2.0969, while the DNN model can predict wear state at an accuracy of 89.1%, with average testing RMSE of 2.5881. The study shows that cropped images of the machined surface profile can be more reliably used to predict the amount of tool flank wear during turning by using the CNN model compared to the height data used in the DNN model.
format Article
author Lim, Meng Lip
Mohd Naqib, Derani
Ratnam, Mani Maran
Ahmad Razlan, Yusoff
author_facet Lim, Meng Lip
Mohd Naqib, Derani
Ratnam, Mani Maran
Ahmad Razlan, Yusoff
author_sort Lim, Meng Lip
title Tool wear prediction in turning using workpiece surface profile images and deep learning neural networks
title_short Tool wear prediction in turning using workpiece surface profile images and deep learning neural networks
title_full Tool wear prediction in turning using workpiece surface profile images and deep learning neural networks
title_fullStr Tool wear prediction in turning using workpiece surface profile images and deep learning neural networks
title_full_unstemmed Tool wear prediction in turning using workpiece surface profile images and deep learning neural networks
title_sort tool wear prediction in turning using workpiece surface profile images and deep learning neural networks
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2022
url http://umpir.ump.edu.my/id/eprint/42653/1/Tool%20wear%20prediction%20in%20turning%20using%20workpiece%20surface.pdf
http://umpir.ump.edu.my/id/eprint/42653/2/Tool%20wear%20prediction%20in%20turning%20using%20workpiece%20surface%20profile%20images%20and%20deep%20learning%20neural%20networks_ABS.pdf
http://umpir.ump.edu.my/id/eprint/42653/
https://doi.org/10.1007/s00170-022-09257-2
https://doi.org/10.1007/s00170-022-09257-2
_version_ 1822924875376885760
score 13.232414

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