A Comparative Study on Machine Learning Algorithms for Smart Manufacturing: Tool Wear Prediction Using Random Forests
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Connor Jennings | Dazhong Wu | Janis Terpenny | Robert X. Gao | Soundar Kumara | R. Gao | J. Terpenny | S. Kumara | Dazhong Wu | Connor Jennings
[1] Tuğrul Özel,et al. Machine Learning Based Predictive Modeling of Machining Induced Microhardness and Grain Size in Ti–6Al–4V Alloy , 2015 .
[2] Taejin Kim,et al. Semi-supervised learning with co-training for data-driven prognostics , 2012, 2012 IEEE Conference on Prognostics and Health Management.
[3] Durmus Karayel,et al. Prediction and control of surface roughness in CNC lathe using artificial neural network , 2009 .
[4] Robert X. Gao,et al. Stochastic Tool Wear Prediction for Sustainable Manufacturing , 2016 .
[5] A. S. Varadarajan,et al. A multi-sensor fusion model based on artificial neural network to predict tool wear during hard turning , 2012 .
[6] Soundarr T. Kumara,et al. Flank Wear Estimation in Turning Through Wavelet Representation of Acoustic Emission Signals , 2000 .
[7] Kai Goebel,et al. Model-Based Prognostics With Concurrent Damage Progression Processes , 2013, IEEE Transactions on Systems, Man, and Cybernetics: Systems.
[8] Steven Y. Liang,et al. Modeling of CBN Tool Flank Wear Progression in Finish Hard Turning , 2004 .
[9] Jay Lee,et al. Machine performance monitoring and proactive maintenance in computer-integrated manufacturing: review and perspective , 1995 .
[10] Donghua Zhou,et al. A Wiener-process-based degradation model with a recursive filter algorithm for remaining useful life estimation , 2013 .
[11] Krzysztof Jemielniak,et al. Advanced monitoring of machining operations , 2010 .
[12] Noureddine Zerhouni,et al. Health assessment and life prediction of cutting tools based on support vector regression , 2015, J. Intell. Manuf..
[13] Antanas Verikas,et al. Mining data with random forests: A survey and results of new tests , 2011, Pattern Recognit..
[14] Gérard Biau,et al. Analysis of a Random Forests Model , 2010, J. Mach. Learn. Res..
[15] David Dornfeld,et al. Tool Wear Detection Using Time Series Analysis of Acoustic Emission , 1989 .
[16] Satish T. S. Bukkapatnam,et al. Fractal Estimation of Flank Wear in Turning , 2000 .
[17] George J. Vachtsevanos,et al. A particle-filtering approach for on-line fault diagnosis and failure prognosis , 2009 .
[18] Ichiro Inasaki,et al. Tool Condition Monitoring (TCM) — The Status of Research and Industrial Application , 1995 .
[19] Chatchapol Chungchoo,et al. On-line tool wear estimation in CNC turning operations , 2001 .
[20] Tuğrul Özel,et al. Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks , 2005 .
[21] Frederick Winslow Taylor,et al. On The Art Of Cutting Metals.pdf , 2017 .
[22] S. Shanmugasundaram,et al. Prediction of tool wear using regression and ANN models in end-milling operation , 2008 .
[23] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[24] Robert X. Gao,et al. Cloud-enabled prognosis for manufacturing , 2015 .
[25] David Dornfeld,et al. A self-organizing approach to the prediction and detection of tool wear , 1998 .
[26] M. J. Er,et al. Fuzzy Neural Network Modelling for Tool Wear Estimation in Dry Milling Operation , 2009 .
[27] Durul Ulutan,et al. Stochastic tool wear assessment in milling difficult to machine alloys , 2015 .
[28] C. Sanjay,et al. Modeling of tool wear in drilling by statistical analysis and artificial neural network , 2005 .
[29] N. R. Sakthivel,et al. Evaluation of expert system for condition monitoring of a single point cutting tool using principle component analysis and decision tree algorithm , 2011, Expert Syst. Appl..
[30] Joseph C. Chen,et al. An artificial-neural-networks-based in-process tool wear prediction system in milling operations , 2005 .
[31] Dongfeng Shi,et al. Tool wear predictive model based on least squares support vector machines , 2007 .
[32] Satish T. S. Bukkapatnam,et al. Analysis of acoustic emission signals in machining , 1999 .
[33] Sohyung Cho,et al. Tool breakage detection using support vector machine learning in a milling process , 2005 .
[34] Soundar Kumara,et al. Machinery Fault Diagnosis and Prognosis: Application of Advanced Signal Processing Techniques , 1999 .
[35] L. Swanson. Linking maintenance strategies to performance , 2001 .
[36] Mark Schwabacher,et al. A Survey of Data -Driven Prognostics , 2005 .
[37] Marcello Braglia,et al. The analytic hierarchy process applied to maintenance strategy selection , 2000, Reliab. Eng. Syst. Saf..
[38] David He,et al. Hidden semi-Markov model-based methodology for multi-sensor equipment health diagnosis and prognosis , 2007, Eur. J. Oper. Res..
[39] Lakhtakia,et al. Analysis of sensor signals shows turning on a lathe exhibits low-dimensional chaos. , 1995, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.
[40] Andy Liaw,et al. Classification and Regression by randomForest , 2007 .
[41] Dazhong Wu,et al. Fog-Enabled Architecture for Data-Driven Cyber-Manufacturing Systems , 2016 .
[42] Richard M. Feldman,et al. A survey of preventive maintenance models for stochastically deteriorating single-unit systems , 1989 .
[43] Corinna Cortes,et al. Support-Vector Networks , 1995, Machine Learning.
[44] Kai Goebel,et al. Comparison of prognostic algorithms for estimating remaining useful life of batteries , 2009 .
[45] Kai Goebel,et al. A Survey of Artificial Intelligence for Prognostics , 2007, AAAI Fall Symposium: Artificial Intelligence for Prognostics.
[46] Robert X. Gao,et al. Adaptive resampling-based particle filtering for tool life prediction , 2015 .
[47] Bernhard Sick,et al. ON-LINE AND INDIRECT TOOL WEAR MONITORING IN TURNING WITH ARTIFICIAL NEURAL NETWORKS: A REVIEW OF MORE THAN A DECADE OF RESEARCH , 2002 .
[48] Alexander J. Smola,et al. Support Vector Regression Machines , 1996, NIPS.
[49] Farbod Akhavan Niaki,et al. State of health monitoring in machining: Extended Kalman filter for tool wear assessment in turning of IN718 hard-to-machine alloy , 2016 .