Cutting tool wear monitoring based on a smart toolholder with embedded force and vibration sensors and an improved residual network
出版年份 2022 全文链接
标题
Cutting tool wear monitoring based on a smart toolholder with embedded force and vibration sensors and an improved residual network
作者
关键词
-
出版物
MEASUREMENT
Volume 199, Issue -, Pages 111520
出版商
Elsevier BV
发表日期
2022-06-19
DOI
10.1016/j.measurement.2022.111520
参考文献
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注意：仅列出部分参考文献，下载原文获取全部文献信息。- Evaluation of transducer signature selections on machine learning performance in cutting tool wear prognosis
- (2022) I.-Chun Sun et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Artificial intelligence enabled smart machining and machine tools
- (2022) Yu Sung Chuo et al. Journal of Mechanical Science and Technology
- Indirect monitoring of machining characteristics via advanced sensor systems: a critical review
- (2022) Mehmet Erdi Korkmaz et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Artificial intelligence systems for tool condition monitoring in machining: analysis and critical review
- (2022) Danil Yu Pimenov et al. JOURNAL OF INTELLIGENT MANUFACTURING
- Performance evaluation of deep e-CNN with integrated spatial-spectral features in hyperspectral image classification
- (2022) M Kavitha et al. MEASUREMENT
- Online chatter detection in milling process based on fast iterative VMD and energy ratio difference
- (2022) Pengfei Zhang et al. MEASUREMENT
- A novel smart toolholder with embedded force sensors for milling operations
- (2022) Pengfei Zhang et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- A novel ensemble deep learning model for cutting tool wear monitoring using audio sensors
- (2022) Zhixiong Li et al. Journal of Manufacturing Processes
- A GAPSO-Enhanced Extreme Learning Machine Method for Tool Wear Estimation in Milling Processes Based on Vibration Signals
- (2021) Zhi Lei et al. International Journal of Precision Engineering and Manufacturing-Green Technology
- A state-of-the-art review on sensors and signal processing systems in mechanical machining processes
- (2021) Mustafa Kuntoğlu et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Intelligent tool wear monitoring based on parallel residual and stacked bidirectional long short-term memory network
- (2021) Xianli Liu et al. JOURNAL OF MANUFACTURING SYSTEMS
- Tool wear monitoring of TC4 titanium alloy milling process based on multi-channel signal and time-dependent properties by using deep learning
- (2021) Boling Yan et al. JOURNAL OF MANUFACTURING SYSTEMS
- A data-driven approach for tool wear recognition and quantitative prediction based on radar map feature fusion
- (2021) Xuebing Li et al. MEASUREMENT
- System for Tool-Wear Condition Monitoring in CNC Machines under Variations of Cutting Parameter Based on Fusion Stray Flux-Current Processing
- (2021) Arturo Yosimar Jaen-Cuellar et al. SENSORS
- Development and testing of a wireless rotating triaxial vibration measuring tool holder system for milling process
- (2020) Chang'an Zhou et al. MEASUREMENT
- A multi-sensor integrated smart tool holder for cutting process monitoring
- (2020) Zhengyou Xie et al. The International Journal of Advanced Manufacturing Technology
- Machine-learning for automatic prediction of flatness deviation considering the wear of the face mill teeth
- (2020) Andres Bustillo et al. JOURNAL OF INTELLIGENT MANUFACTURING
- CNN-LSTM deep learning architecture for computer vision-based modal frequency detection
- (2020) Ruoyu Yang et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- A tool condition monitoring method based on two-layer angle kernel extreme learning machine and binary differential evolution for milling
- (2020) Yuqing Zhou et al. MEASUREMENT
- A Review of Indirect Tool Condition Monitoring Systems and Decision-Making Methods in Turning: Critical Analysis and Trends
- (2020) Mustafa Kuntoğlu et al. SENSORS
- Development of tool condition monitoring system in end milling process using wavelet features and Hoelder’s exponent with machine learning algorithms
- (2020) T. Mohanraj et al. MEASUREMENT
- Cutting Tool Wear Monitoring in CNC Machines Based in Spindle-Motor Stray Flux Signals
- (2020) Israel Zamudio-Ramirez et al. IEEE Transactions on Industrial Informatics
- An improved deep convolutional neural network with multi-scale information for bearing fault diagnosis
- (2019) Wenyi Huang et al. NEUROCOMPUTING
- Smart-sensor for tool-breakage detection in milling process under dry and wet conditions based on infrared thermography
- (2018) Juan A. Ramirez-Nunez et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Automatic autonomous vision-based power line inspection: A review of current status and the potential role of deep learning
- (2018) Van Nhan Nguyen et al. INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
- Predicting tool life in turning operations using neural networks and image processing
- (2018) T. Mikołajczyk et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Gaussian process regression for tool wear prediction
- (2018) Dongdong Kong et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load
- (2018) Wei Zhang et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- A wireless instrumented milling cutter system with embedded PVDF sensors
- (2018) Ming Luo et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Tool condition monitoring using spectral subtraction and convolutional neural networks in milling process
- (2018) Fatemeh Aghazadeh et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Deep learning and its applications to machine health monitoring
- (2018) Rui Zhao et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth
- (2017) D. Yu. Pimenov et al. JOURNAL OF INTELLIGENT MANUFACTURING
- Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks
- (2017) Osama Abdeljaber et al. JOURNAL OF SOUND AND VIBRATION
- Development and testing of an integrated smart tool holder for four-component cutting force measurement
- (2017) Zhengyou Xie et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Neural network approach for automatic image analysis of cutting edge wear
- (2017) T. Mikołajczyk et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Tool wear assessment based on type-2 fuzzy uncertainty estimation on acoustic emission
- (2015) Qun Ren et al. APPLIED SOFT COMPUTING
- Development and testing of an integrated rotating dynamometer on tool holder for milling process
- (2015) Muhammad Rizal et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- PVDF sensor-based monitoring of milling torque
- (2013) Lei Ma et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups
- (2012) Geoffrey Hinton et al. IEEE SIGNAL PROCESSING MAGAZINE
- Development of a dynamometer for measuring individual cutting edge forces in face milling
- (2010) G. Totis et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
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