Tool wear prediction using long short-term memory variants and hybrid feature selection techniques

[1]  K. Kotecha,et al.  Performance evaluation for tool wear prediction based on Bi-directional, Encoder–Decoder and Hybrid Long Short-Term Memory models , 2022, International Journal of Quality & Reliability Management.

[2]  Sheng Wang,et al.  Heterogeneous sensors-based feature optimisation and deep learning for tool wear prediction , 2021, The International Journal of Advanced Manufacturing Technology.

[3]  N. He,et al.  Development and Testing of a High-Frequency Dynamometer for High-Speed Milling Process , 2021, Machines.

[4]  Tadeusz Mikolajczyk,et al.  A Review of Indirect Tool Condition Monitoring Systems and Decision-Making Methods in Turning: Critical Analysis and Trends , 2020, Sensors.

[5]  Chih-Chun Cheng,et al.  Feature selection for predicting tool wear of machine tools , 2020, The International Journal of Advanced Manufacturing Technology.

[6]  Yongli Wei,et al.  A hybrid predictive maintenance approach for CNC machine tool driven by Digital Twin , 2020, Robotics Comput. Integr. Manuf..

[7]  Xifan Yao,et al.  Multi-Sensor Data Fusion for Remaining Useful Life Prediction of Machining Tools by IABC-BPNN in Dry Milling Operations , 2020, Sensors.

[8]  Yun-Chih Lin,et al.  Prediction of Surface Roughness Based on Cutting Parameters and Machining Vibration in End Milling Using Regression Method and Artificial Neural Network , 2020, Applied Sciences.

[9]  Weifang Sun,et al.  Tool Wear Condition Monitoring in Milling Process Based on Current Sensors , 2020, IEEE Access.

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

[11]  Ta-Wen Kuan,et al.  Outpatient Text Classification Using Attention-Based Bidirectional LSTM for Robot-Assisted Servicing in Hospital , 2020, Inf..

[12]  Xiaofeng Hu,et al.  A hybrid information model based on long short-term memory network for tool condition monitoring , 2020, Journal of Intelligent Manufacturing.

[13]  Nikola S. Nikolov,et al.  Feature selection methods and genomic big data: a systematic review , 2019, Journal of Big Data.

[14]  Derya Birant,et al.  Remaining Useful Life Estimation for Predictive Maintenance Using Feature Engineering , 2019, 2019 Innovations in Intelligent Systems and Applications Conference (ASYU).

[15]  Giha Lee,et al.  Application of Long Short-Term Memory (LSTM) Neural Network for Flood Forecasting , 2019, Water.

[16]  Meiqing Wang,et al.  Machined Surface Quality Monitoring Using a Wireless Sensory Tool Holder in the Machining Process , 2019, Sensors.

[17]  T. Y. Wu,et al.  Prediction of surface roughness in milling process using vibration signal analysis and artificial neural network , 2019, The International Journal of Advanced Manufacturing Technology.

[18]  Wei Xue,et al.  A Multisensor Fusion Method for Tool Condition Monitoring in Milling , 2018, Sensors.

[19]  Alessandra Caggiano,et al.  Tool Wear Prediction in Ti-6Al-4V Machining through Multiple Sensor Monitoring and PCA Features Pattern Recognition , 2018, Sensors.

[20]  Liang Guo,et al.  A recurrent neural network based health indicator for remaining useful life prediction of bearings , 2017, Neurocomputing.

[21]  Ruqiang Yan,et al.  Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks , 2017, Sensors.

[22]  Michael G. Pecht,et al.  IoT-Based Prognostics and Systems Health Management for Industrial Applications , 2016, IEEE Access.

[23]  José Ortiz,et al.  Model-based fault detection and diagnosis in ALMA subsystems , 2016, Astronomical Telescopes + Instrumentation.

[24]  Xifan Yao,et al.  Tool Condition Monitoring and Remaining Useful Life Prognostic Based on a Wireless Sensor in Dry Milling Operations , 2016, Sensors.

[25]  D. Asir Antony Gnana Singh,et al.  Literature Review on Feature Selection Methods for High-Dimensional Data , 2016 .

[26]  Che Hassan Che Haron,et al.  Development and testing of an integrated rotating dynamometer on tool holder for milling process , 2015 .

[27]  Mohd. Zaki Nuawi,et al.  A Review of Sensor System and Application in Milling Process for Tool Condition Monitoring , 2014 .

[28]  Guofeng Wang,et al.  Vibration sensor based tool condition monitoring using ν support vector machine and locality preserving projection , 2014 .

[29]  Mathieu Ritou,et al.  Angular approach combined to mechanical model for tool breakage detection by eddy current sensors , 2014 .

[30]  Surjya K. Pal,et al.  Correlation study of tool flank wear with machined surface texture in end milling , 2013 .

[31]  Fulei Chu,et al.  Recent advances in time–frequency analysis methods for machinery fault diagnosis: A review with application examples , 2013 .

[32]  Meng Joo Er,et al.  Adaptive Network Fuzzy Inference System and support vector machine learning for tool wear estimation in high speed milling processes , 2012, IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society.

[33]  R. A. Khan,et al.  MFCC and Prosodic Feature Extraction Techniques: A Comparative Study , 2012 .

[34]  Minqiang Xu,et al.  Reliability-based maintenance optimization under imperfect predictive maintenance , 2012 .

[35]  Xiaoli Li,et al.  Time-frequency-analysis-based minor cutting edge fracture detection during end milling , 2004 .

[36]  D. E. Dimla,et al.  Sensor signals for tool-wear monitoring in metal cutting operations—a review of methods , 2000 .

[37]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[38]  Ketan Kotecha,et al.  Data-Driven Remaining Useful Life Estimation for Milling Process: Sensors, Algorithms, Datasets, and Future Directions , 2021, IEEE Access.

[39]  Sri Addepalli,et al.  Remaining Useful Life Prediction using Deep Learning Approaches: A Review , 2020, Procedia Manufacturing.

[40]  N. R. Sakthivel,et al.  Tool condition monitoring techniques in milling process — a review , 2020 .

[41]  Ivo Paixao de Medeiros,et al.  Forecasting fault events for predictive maintenance using data-driven techniques and ARMA modeling , 2018, Comput. Ind. Eng..

[42]  Runliang Dou,et al.  Dynamic deep learning algorithm based on incremental compensation for fault diagnosis model , 2018, Int. J. Comput. Intell. Syst..

[43]  et al. Tahir Extracting accurate time domain features from vibration signals for reliable classification of bearing faults , 2018 .

[44]  Suresh Thenozhi,et al.  Frequency and Time-Frequency Analysis of Cutting Force and Vibration Signals for Tool Condition Monitoring , 2018, IEEE Access.

[45]  Konrad Wegener,et al.  Condition-based Maintenance: Model vs. Statistics a Performance Comparison , 2016 .

[46]  David Nettleton,et al.  Selection of Variables and Factor Derivation , 2014 .

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

[48]  John Dempster,et al.  The Laboratory Computer: A Practical Guide for Physiologists and Neuroscientists , 2001 .

[49]  Paul William Prickett,et al.  An overview of approaches to end milling tool monitoring , 1999 .