See the published version of this preprint at The International Journal of Advanced Manufacturing Technology.
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Research Article
Nan Xie
Tongji University - Jiading Campus
Corresponding Author
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Tool wear condition (TCM) has a significant impact on machining quality, efficiency and cost, so it is vitally important in manufacturing systems. The current work of TCM mainly process the time series signals from multisensory by intelligent algorithm. However, the limited of these method is; 1) the image information are not integrated into the time series signals, 2) the traditional methods are facing the problems of poor generalization and fast convergence. Thus, a novel integrated model based on the multisensory feature fusion and neural network is presented. After the PAA pre-processed, the developed model recodes the sensor data into images using GAF and inputs to CNN model with the tool infrared images, then realizes the output of flank wear value. Compared with other algorithms, the results show that the algorithm has better classification ability.
Keywords
Tool Condition Monitoring, Deep Learning, Turning
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CURRENT STATUS: Published
View the published article at The International Journal of Advanced Manufacturing Technology.
Version 1
Posted 12 May, 2021
No community comments so far
Editorial decision: Major Revisions Needed
On 22 Jun, 2021
Reviews received
Received 19 May, 2021
Reviewers invited
Invitations sent on 07 May, 2021
Editor assigned
On 05 May, 2021
First submitted
On 04 May, 2021
Metrics
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Subject Areas
Mechanical Engineering
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A new preprint design is coming!
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See the published version of this preprint at The International Journal of Advanced Manufacturing Technology.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Nan Xie
Tongji University - Jiading Campus
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at The International Journal of Advanced Manufacturing Technology.
Version 1
Posted 12 May, 2021
No community comments so far
Editorial decision: Major Revisions Needed
On 22 Jun, 2021
Reviews received
Received 19 May, 2021
Reviewers invited
Invitations sent on 07 May, 2021
Editor assigned
On 05 May, 2021
First submitted
On 04 May, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Mechanical Engineering
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at The International Journal of Advanced Manufacturing Technology.
Tool wear condition (TCM) has a significant impact on machining quality, efficiency and cost, so it is vitally important in manufacturing systems. The current work of TCM mainly process the time series signals from multisensory by intelligent algorithm. However, the limited of these method is; 1) the image information are not integrated into the time series signals, 2) the traditional methods are facing the problems of poor generalization and fast convergence. Thus, a novel integrated model based on the multisensory feature fusion and neural network is presented. After the PAA pre-processed, the developed model recodes the sensor data into images using GAF and inputs to CNN model with the tool infrared images, then realizes the output of flank wear value. Compared with other algorithms, the results show that the algorithm has better classification ability.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
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