Analysis of robustness and transferability in feature-based grinding burn detection
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Title
Analysis of robustness and transferability in feature-based grinding burn detection
Authors
Keywords
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Journal
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
Volume 120, Issue 3-4, Pages 2587-2602
Publisher
Springer Science and Business Media LLC
Online
2022-02-24
DOI
10.1007/s00170-022-08834-9
References
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Related references
Note: Only part of the references are listed.- Hinkley criterion applied to detection and location of burn in grinding process
- (2021) Carine G. Távora et al. The International Journal of Advanced Manufacturing Technology
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- (2021) Emil Sauter et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- A robust condition monitoring methodology for grinding wheel wear identification using Hilbert Huang transform
- (2021) Supriyo Mahata et al. PRECISION ENGINEERING-JOURNAL OF THE INTERNATIONAL SOCIETIES FOR PRECISION ENGINEERING AND NANOTECHNOLOGY
- Automatic grinding burn recognition based on time-frequency analysis and convolutional neural networks
- (2020) Henrique Butzlaff Hübner et al. The International Journal of Advanced Manufacturing Technology
- An intelligent grinding burn detection system based on two-stage feature selection and stacked sparse autoencoder
- (2019) Weicheng Guo et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Grinding Burn Detection Based on Cross Wavelet and Wavelet Coherence Analysis by Acoustic Emission Signal
- (2019) Zheyu Gao et al. Chinese Journal of Mechanical Engineering
- Adaptive Grinding Process—Prevention of Thermal Damage Using OPC-UA Technique and In Situ Metrology
- (2017) Matthias Steffan et al. JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME
- Monitoring of grinding burn via Barkhausen noise emission in case-hardened steel in large-bearing production
- (2017) Miroslav Neslušan et al. JOURNAL OF MATERIALS PROCESSING TECHNOLOGY
- Online evaluation of metal burn degrees based on acoustic emission and variational mode decomposition
- (2017) Zheyu Gao et al. MEASUREMENT
- Taking the Human Out of the Loop: A Review of Bayesian Optimization
- (2016) Bobak Shahriari et al. PROCEEDINGS OF THE IEEE
- A non-destructive surface burn detection method for ferrous metals based on acoustic emission and ensemble empirical mode decomposition: from laser simulation to grinding process
- (2014) Zhensheng Yang et al. MEASUREMENT SCIENCE and TECHNOLOGY
- Experimental study of burn classification and prediction using indirect method in surface grinding of AISI 1045 steel
- (2013) Zhensheng Yang et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Burn threshold prediction for High Efficiency Deep Grinding
- (2011) A. Bell et al. INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE
- Multiple classification of the acoustic emission signals extracted during burn and chatter anomalies using genetic programming
- (2009) James Marcus Griffin et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Gear finishing by abrasive processes
- (2008) B. Karpuschewski et al. CIRP ANNALS-MANUFACTURING TECHNOLOGY
- Automated Sensor Selection and Fusion for Monitoring and Diagnostics of Plunge Grinding
- (2008) Niranjan Subrahmanya et al. JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME
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