Segmentation and quantitative evaluation for tool wear condition via an improved SE-U-Net
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Title
Segmentation and quantitative evaluation for tool wear condition via an improved SE-U-Net
Authors
Keywords
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Journal
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
Volume -, Issue -, Pages -
Publisher
Springer Science and Business Media LLC
Online
2022-05-17
DOI
10.1007/s00170-022-09338-2
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- (2020) J. Kalisz et al. TRIBOLOGY INTERNATIONAL
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- (2019) Shipeng Fu et al. CIRCUITS SYSTEMS AND SIGNAL PROCESSING
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- (2019) T. Mohanraj et al. Journal of Materials Research and Technology-JMR&T
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- Review of tool condition monitoring methods in milling processes
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- (2018) Wentao Zhu et al. MEDICAL PHYSICS
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- Incremental learning for online tool condition monitoring using Ellipsoid ARTMAP network model
- (2015) C. Liu et al. APPLIED SOFT COMPUTING
- An investigation of tool wear using acoustic emission and genetic algorithm
- (2014) G Vetrichelvan et al. JOURNAL OF VIBRATION AND CONTROL
- Advanced monitoring of machining operations
- (2010) R. Teti et al. CIRP ANNALS-MANUFACTURING TECHNOLOGY
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