Hybrid machine learning-enabled multi-information fusion for indirect measurement of tool flank wear in milling
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Title
Hybrid machine learning-enabled multi-information fusion for indirect measurement of tool flank wear in milling
Authors
Keywords
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Journal
MEASUREMENT
Volume 206, Issue -, Pages 112255
Publisher
Elsevier BV
Online
2022-11-25
DOI
10.1016/j.measurement.2022.112255
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- (2022) Kangping Gao et al. ENGINEERING FAILURE ANALYSIS
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- Application of measurement systems in tool condition monitoring of Milling: A review of measurement science approach
- (2022) Danil Yu. Pimenov et al. MEASUREMENT
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- (2022) Mohammad Zhian Asadzadeh et al. Journal of Manufacturing Processes
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- (2022) Kai Li et al. EXPERT SYSTEMS WITH APPLICATIONS
- Sound singularity analysis for milling tool condition monitoring towards sustainable manufacturing
- (2021) Chang'an Zhou et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Tool wear prediction method based on symmetrized dot pattern and multi-covariance Gaussian process regression
- (2021) Chuandong Zhang et al. MEASUREMENT
- A hybrid information model based on long short-term memory network for tool condition monitoring
- (2020) Weili Cai et al. JOURNAL OF INTELLIGENT MANUFACTURING
- Technical data-driven tool condition monitoring challenges for CNC milling: a review
- (2020) Shi Yuen Wong et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Research on tool wear monitoring in drilling process based on APSO-LS-SVM approach
- (2020) Ni Chen et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Tool condition monitoring in milling process using multifractal detrended fluctuation analysis and support vector machine
- (2020) Jingchao Guo et al. The International Journal of Advanced Manufacturing Technology
- Investigation of signal behaviors for sensor fusion with tool condition monitoring system in turning
- (2020) Mustafa Kuntoğlu et al. MEASUREMENT
- Multi-Sensor Data Fusion for Remaining Useful Life Prediction of Machining Tools by IABC-BPNN in Dry Milling Operations
- (2020) Min Liu et al. SENSORS
- Intelligent monitoring and diagnostics using a novel integrated model based on deep learning and multi-sensor feature fusion
- (2020) Xingwei Xu et al. MEASUREMENT
- PSO-LSSVR: A surrogate modeling approach for probabilistic flutter evaluation of compressor blade
- (2020) Bo-Wei Wang et al. Structures
- Multi-Sensor Data-Driven Remaining Useful Life Prediction of Semi-Observable Systems
- (2020) Naipeng Li et al. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
- Tool wear predicting based on multi-domain feature fusion by deep convolutional neural network in milling operations
- (2019) Zhiwen Huang et al. JOURNAL OF INTELLIGENT MANUFACTURING
- Monitoring of a machining process using kernel principal component analysis and kernel density estimation
- (2019) Wo Jae Lee et al. JOURNAL OF INTELLIGENT MANUFACTURING
- Monitoring tool wear using wavelet package decomposition and a novel gravitational search algorithm–least square support vector machine model
- (2019) Dongdong Kong et al. PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE
- Tool condition monitoring techniques in milling process — a review
- (2019) T. Mohanraj et al. Journal of Materials Research and Technology-JMR&T
- Multi-sensor information fusion for remaining useful life prediction of machining tools by adaptive network based fuzzy inference system
- (2018) Jun Wu et al. APPLIED SOFT COMPUTING
- Review of tool condition monitoring methods in milling processes
- (2018) Yuqing Zhou et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- A machine vision system for micro-milling tool condition monitoring
- (2018) Yiquan Dai et al. PRECISION ENGINEERING-JOURNAL OF THE INTERNATIONAL SOCIETIES FOR PRECISION ENGINEERING AND NANOTECHNOLOGY
- The relationships between cutting parameters, tool wear, cutting force and vibration
- (2018) Xu Chuangwen et al. Advances in Mechanical Engineering
- A generic tool wear model and its application to force modeling and wear monitoring in high speed milling
- (2018) Kunpeng Zhu et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- A review of helical milling process
- (2017) Robson Bruno Dutra Pereira et al. INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE
- Multisensory fusion based virtual tool wear sensing for ubiquitous manufacturing
- (2017) Jinjiang Wang et al. ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
- Cutting tool wear classification and detection using multi-sensor signals and Mahalanobis-Taguchi System
- (2017) M. Rizal et al. WEAR
- Milling tool wear diagnosis by feed motor current signal using an artificial neural network
- (2016) Mehrdad Nouri Khajavi et al. Journal of Mechanical Science and Technology
- A novel monitoring method for turning tool wear based on support vector machines
- (2016) Songsong Yang et al. PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE
- A new predictive model based on the PSO-optimized support vector machine approach for predicting the milling tool wear from milling runs experimental data
- (2015) P. J. García-Nieto et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Real-time tool wear monitoring in milling using a cutting condition independent method
- (2015) Mehdi Nouri et al. INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE
- A method for tool condition monitoring based on sensor fusion
- (2015) Kai-feng Zhang et al. JOURNAL OF INTELLIGENT MANUFACTURING
- Type-2 fuzzy tool condition monitoring system based on acoustic emission in micromilling
- (2013) Qun Ren et al. INFORMATION SCIENCES
- Health assessment and life prediction of cutting tools based on support vector regression
- (2013) T. Benkedjouh et al. JOURNAL OF INTELLIGENT MANUFACTURING
- A multi-sensor fusion model based on artificial neural network to predict tool wear during hard turning
- (2012) P Sam Paul et al. PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE
- Application of backpropagation neural network for spindle vibration-based tool wear monitoring in micro-milling
- (2011) Wan-Hao Hsieh et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Tool wear condition monitoring using a sensor fusion model based on fuzzy inference system
- (2008) Cuneyt Aliustaoglu et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
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