A hybrid remaining useful life prediction method for cutting tool considering the wear state

[1]  Weifang Zhang,et al.  Remaining Useful Life Prediction of Cutting Tools Using an Inverse Gaussian Process Model , 2021, Applied Sciences.

[2]  Chris K. Mechefske,et al.  Tool wear prediction in high-speed turning of a steel alloy using long short-term memory modelling , 2021 .

[3]  Weiwei Ming,et al.  Deep learning-based tool wear prediction and its application for machining process using multi-scale feature fusion and channel attention mechanism , 2021, Measurement.

[4]  Robert X. Gao,et al.  Physics guided neural network for machining tool wear prediction , 2020 .

[5]  Ziwei Li,et al.  A novel approach for predicting tool remaining useful life using limited data , 2020 .

[6]  Minghui Cheng,et al.  An intelligent prediction model of the tool wear based on machine learning in turning high strength steel , 2020 .

[7]  Yaguo Lei,et al.  Applications of machine learning to machine fault diagnosis: A review and roadmap , 2020 .

[8]  Huibin Sun,et al.  Non-linear Wiener process–based cutting tool remaining useful life prediction considering measurement variability , 2020 .

[9]  Ming Chen,et al.  A data-driven model for milling tool remaining useful life prediction with convolutional and stacked LSTM network , 2020 .

[10]  Huibin Sun,et al.  Enhancing cutting tool sustainability based on remaining useful life prediction , 2020 .

[11]  Kai Guo,et al.  Vibration singularity analysis for milling tool condition monitoring , 2020 .

[12]  Liang Li,et al.  Research on the milling tool wear and life prediction by establishing an integrated predictive model , 2019, Measurement.

[13]  Paweł Twardowski,et al.  Prediction of Tool Wear Using Artificial Neural Networks during Turning of Hardened Steel , 2019, Materials.

[14]  Weijian Li,et al.  Time varying and condition adaptive hidden Markov model for tool wear state estimation and remaining useful life prediction in micro-milling , 2019, Mechanical Systems and Signal Processing.

[15]  Yi Dong,et al.  Tool life prediction based on Gauss importance resampling particle filter , 2019, The International Journal of Advanced Manufacturing Technology.

[16]  Juan Lu,et al.  Tool wear state recognition based on GWO–SVM with feature selection of genetic algorithm , 2019, The International Journal of Advanced Manufacturing Technology.

[17]  Aldo Attanasio,et al.  Tool wear analysis in micromilling of titanium alloy , 2019, Precision Engineering.

[18]  Wennian Yu,et al.  Hybrid data-driven physics-based model fusion framework for tool wear prediction , 2018, The International Journal of Advanced Manufacturing Technology.

[19]  Huibin Sun,et al.  A Hybrid Approach to Cutting Tool Remaining Useful Life Prediction Based on the Wiener Process , 2018, IEEE Transactions on Reliability.

[20]  Changqing Liu,et al.  Real-time cutting tool state recognition approach based on machining features in NC machining process of complex structural parts , 2018 .

[21]  A. M. M. Sharif Ullah,et al.  Tool-wear prediction and pattern-recognition using artificial neural network and DNA-based computing , 2015, Journal of Intelligent Manufacturing.

[22]  Shuang Liang,et al.  A weighted hidden Markov model approach for continuous-state tool wear monitoring and tool life prediction , 2016, The International Journal of Advanced Manufacturing Technology.

[23]  M. Jackson,et al.  On the mechanism of tool crater wear during titanium alloy machining , 2017 .

[24]  Dongdong Kong,et al.  Tool wear monitoring based on kernel principal component analysis and v-support vector regression , 2016, The International Journal of Advanced Manufacturing Technology.

[25]  Linxia Liao,et al.  A hybrid framework combining data-driven and model-based methods for system remaining useful life prediction , 2016, Appl. Soft Comput..

[26]  Robert X. Gao,et al.  Adaptive resampling-based particle filtering for tool life prediction , 2015 .

[27]  Robert X. Gao,et al.  Enhanced particle filter for tool wear prediction , 2015 .

[28]  Petar M. Djuric,et al.  Resampling Methods for Particle Filtering: Classification, implementation, and strategies , 2015, IEEE Signal Processing Magazine.

[29]  Noureddine Zerhouni,et al.  Health assessment and life prediction of cutting tools based on support vector regression , 2015, J. Intell. Manuf..

[30]  Chee Khiang Pang,et al.  PDF and Breakdown Time Prediction for Unobservable Wear Using Enhanced Particle Filters in Precognitive Maintenance , 2015, IEEE Transactions on Instrumentation and Measurement.

[31]  Yoo Hyun Park,et al.  Time-slide window join over data streams , 2014, Journal of Intelligent Information Systems.

[32]  Z. Pálmai,et al.  Proposal for a new theoretical model of the cutting tool's flank wear , 2013 .

[33]  Khaled Abou-El-Hossein,et al.  Tool life and wear mechanism when machining Hastelloy C-22HS , 2011 .

[34]  S. K. Choudhury,et al.  Tool wear prediction in turning , 2004 .

[35]  Taylan Altan,et al.  Estimation of tool wear in orthogonal cutting using the finite element analysis , 2004 .

[36]  Athanasios G. Mamalis,et al.  Wear and Tool Life of CBN Cutting Tools , 2002 .

[37]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[38]  Giacomo Maria Galante,et al.  Tool-life modelling as a stochastic process , 1998 .

[39]  Colin Bradley,et al.  A review of machine vision sensors for tool condition monitoring , 1997 .

[40]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[41]  J. Archard Contact and Rubbing of Flat Surfaces , 1953 .

[42]  H. Attia,et al.  Physics-based approach for predicting dissolution‒diffusion tool wear in machining , 2020, CIRP Annals.

[43]  Kunpeng Zhu,et al.  Online Tool Wear Monitoring Via Hidden Semi-Markov Model With Dependent Durations , 2018, IEEE Transactions on Industrial Informatics.

[44]  M. J. Er,et al.  Fuzzy Neural Network Modelling for Tool Wear Estimation in Dry Milling Operation , 2009 .

[45]  Aldo Attanasio,et al.  3D finite element analysis of tool wear in machining , 2008 .

[46]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .