Tool wear condition monitoring based on multi-sensor integration and deep residual convolution network Article Swipe
Zhiying Zhu
,
Riliang Liu
,
Yunfei Zeng
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1088/2631-8695/acbfa6
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1088/2631-8695/acbfa6
In order to guarantee machining quality and production efficiency and as well as to reduce downtime and cost, it is essential to receive information about the cutting tool condition and change it in time if necessary. To this end, tool condition monitoring systems are of great significance. Based on the data fusion technology of multi-sensor integration and the learning ability of deep residual network, a tool wear monitoring system is proposed in this paper. For data acquisition, multiple sensors are used to collect vibration, noise and acoustic emission signals in the machining process. For signal processing, the data fusion method of angular summation of matrices is used, so that the multi-source data are fused into two-dimensional pictures. Then, the feature extraction module of the proposed system uses the pictures as input and takes advantage of the deep residual convolutional network in extracting the deep features from the pictures, and finally the identification module completes the recognition of the tool wear type. To verify its feasibility, a testing bed is built on a CNC milling machine, and multi-sensor data are collected during the machining process of a simple workpiece with one of the two selected cutting tools each time. The proposed system is trained using the collected data and then is used to monitor the wear condition of a new cutting tool by collecting real-time data under similar condition. The results show that the accuracy of wear state recognition is as high as 90%, which verifies the feasibility of the proposed tool wear monitoring system.
Related Topics
Concepts
Tool wear
Residual
Downtime
Sensor fusion
Computer science
Process (computing)
Machining
Data acquisition
Noise (video)
Cutting tool
Condition monitoring
Machine tool
Real-time computing
Artificial intelligence
Engineering
Mechanical engineering
Algorithm
Electrical engineering
Image (mathematics)
Operating system
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/2631-8695/acbfa6
- https://iopscience.iop.org/article/10.1088/2631-8695/acbfa6/pdf
- OA Status
- bronze
- Cited By
- 9
- References
- 21
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4322488822
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4322488822Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1088/2631-8695/acbfa6Digital Object Identifier
- Title
-
Tool wear condition monitoring based on multi-sensor integration and deep residual convolution networkWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-02-27Full publication date if available
- Authors
-
Zhiying Zhu, Riliang Liu, Yunfei ZengList of authors in order
- Landing page
-
https://doi.org/10.1088/2631-8695/acbfa6Publisher landing page
- PDF URL
-
https://iopscience.iop.org/article/10.1088/2631-8695/acbfa6/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
-
https://iopscience.iop.org/article/10.1088/2631-8695/acbfa6/pdfDirect OA link when available
- Concepts
-
Tool wear, Residual, Downtime, Sensor fusion, Computer science, Process (computing), Machining, Data acquisition, Noise (video), Cutting tool, Condition monitoring, Machine tool, Real-time computing, Artificial intelligence, Engineering, Mechanical engineering, Algorithm, Electrical engineering, Image (mathematics), Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
9Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 5, 2024: 3, 2023: 1Per-year citation counts (last 5 years)
- References (count)
-
21Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| primary_location.source.host_organization_name | IOP Publishing |
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| primary_location.source.host_organization_lineage_names | IOP Publishing, Institute of Physics |
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| primary_location.pdf_url | https://iopscience.iop.org/article/10.1088/2631-8695/acbfa6/pdf |
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| primary_location.raw_source_name | Engineering Research Express |
| primary_location.landing_page_url | https://doi.org/10.1088/2631-8695/acbfa6 |
| publication_date | 2023-02-27 |
| publication_year | 2023 |
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