Tool wear monitoring by ensemble learning and sensor fusion using power, sound, vibration, and AE signals
Published 2021 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Tool wear monitoring by ensemble learning and sensor fusion using power, sound, vibration, and AE signals
Authors
Keywords
Tool condition monitoring, Wood machining, Extreme cutting conditions, Tool wear classification, Sensor fusion, Ensemble learning
Journal
Manufacturing Letters
Volume 30, Issue -, Pages 32-38
Publisher
Elsevier BV
Online
2021-10-30
DOI
10.1016/j.mfglet.2021.10.002
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Cutting tool temperature monitoring in circular sawing: measurement and multi-sensor feature fusion-based prediction
- (2021) Vahid Nasir et al. The International Journal of Advanced Manufacturing Technology
- Cutting power and surface quality in sawing kiln-dried, green, and frozen hem-fir wood
- (2021) Vahid Nasir et al. WOOD SCIENCE AND TECHNOLOGY
- A review on deep learning in machining and tool monitoring: methods, opportunities, and challenges
- (2021) Vahid Nasir et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Intelligent wood machining monitoring using vibration signals combined with self-organizing maps for automatic feature selection
- (2020) Vahid Nasir et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Characterization, optimization, and acoustic emission monitoring of airborne dust emission during wood sawing
- (2020) Vahid Nasir et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Acoustic emission monitoring of sawing process: artificial intelligence approach for optimal sensory feature selection
- (2019) Vahid Nasir et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Optimal power consumption and surface quality in the circular sawing process of Douglas-fir wood
- (2019) Vahid Nasir et al. European Journal of Wood and Wood Products
- The use of support vector machine, neural network, and regression analysis to predict and optimize surface roughness and cutting forces in milling
- (2019) Ali Yeganefar et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- The Dependence of Surface Quality on Tool Wear of Circular Saw Blades during Transversal Sawing of Beech Wood
- (2015) Richard Kminiak et al. BioResources
- Tool wear predictability estimation in milling based on multi-sensorial data
- (2015) P. Stavropoulos et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Using artificial neural networks for modeling surface roughness of wood in machining process
- (2014) Sebahattin Tiryaki et al. CONSTRUCTION AND BUILDING MATERIALS
- Tool wear in terms of vibration effects in milling medium-density fibreboard with an industrial robot
- (2014) Janez Tratar et al. Journal of Mechanical Science and Technology
- Application of Neural Network in Simple Tool Wear Monitoring and Indentification System in MDF Milling
- (2011) Marcin Zbieć Drvna Industrija
- Advanced monitoring of machining operations
- (2010) R. Teti et al. CIRP ANNALS-MANUFACTURING TECHNOLOGY
- Toward a process monitoring of CNC wood router. Sensor selection and surface roughness prediction
- (2010) Piotr Iskra et al. WOOD SCIENCE AND TECHNOLOGY
Create your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create NowBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started