Cloud-based machine learning for predictive analytics: Tool wear prediction in milling
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Connor Jennings | Dazhong Wu | Janis P. Terpenny | Soundar Kumara | J. Terpenny | S. Kumara | Dazhong Wu | Connor Jennings
[1] Kai Goebel,et al. A Survey of Artificial Intelligence for Prognostics , 2007, AAAI Fall Symposium: Artificial Intelligence for Prognostics.
[2] Kai Goebel,et al. Comparison of prognostic algorithms for estimating remaining useful life of batteries , 2009 .
[3] Satish T. S. Bukkapatnam,et al. Fractal Estimation of Flank Wear in Turning , 2000 .
[4] Mark Schwabacher,et al. A Survey of Data -Driven Prognostics , 2005 .
[5] L. Swanson. Linking maintenance strategies to performance , 2001 .
[6] Kunle Olukotun,et al. Map-Reduce for Machine Learning on Multicore , 2006, NIPS.
[7] Eric R. Ziegel,et al. The Elements of Statistical Learning , 2003, Technometrics.
[8] 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 .
[9] Soundar Kumara,et al. Machinery Fault Diagnosis and Prognosis: Application of Advanced Signal Processing Techniques , 1999 .
[10] M. J. Er,et al. Fuzzy Neural Network Modelling for Tool Wear Estimation in Dry Milling Operation , 2009 .
[11] Tuğrul Özel,et al. Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks , 2005 .
[12] Donghua Zhou,et al. A Wiener-process-based degradation model with a recursive filter algorithm for remaining useful life estimation , 2013 .
[13] Krzysztof Jemielniak,et al. Advanced monitoring of machining operations , 2010 .
[14] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[15] David Dornfeld,et al. A self-organizing approach to the prediction and detection of tool wear , 1998 .
[16] Taejin Kim,et al. Semi-supervised learning with co-training for data-driven prognostics , 2012 .
[17] Ichiro Inasaki,et al. Tool Condition Monitoring (TCM) — The Status of Research and Industrial Application , 1995 .
[18] Taejin Kim,et al. Semi-supervised learning with co-training for data-driven prognostics , 2012, 2012 IEEE Conference on Prognostics and Health Management.
[19] Joseph C. Chen,et al. An artificial-neural-networks-based in-process tool wear prediction system in milling operations , 2005 .
[20] George J. Vachtsevanos,et al. A particle-filtering approach for on-line fault diagnosis and failure prognosis , 2009 .
[21] Chatchapol Chungchoo,et al. On-line tool wear estimation in CNC turning operations , 2001 .
[22] 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..
[23] Sanjay Ghemawat,et al. MapReduce: Simplified Data Processing on Large Clusters , 2004, OSDI.
[24] Robert X. Gao,et al. Cloud-enabled prognosis for manufacturing , 2015 .
[25] Satish T. S. Bukkapatnam,et al. Analysis of acoustic emission signals in machining , 1999 .
[26] Andrzej Stachurski,et al. Parallel Optimization: Theory, Algorithms and Applications , 2000, Parallel Distributed Comput. Pract..
[27] Jay Lee,et al. Machine performance monitoring and proactive maintenance in computer-integrated manufacturing: review and perspective , 1995 .
[28] Richard M. Feldman,et al. A survey of preventive maintenance models for stochastically deteriorating single-unit systems , 1989 .
[29] Joseph M. Hellerstein,et al. GraphLab: A New Framework For Parallel Machine Learning , 2010, UAI.
[30] Marcello Braglia,et al. The analytic hierarchy process applied to maintenance strategy selection , 2000, Reliab. Eng. Syst. Saf..
[31] David He,et al. Hidden semi-Markov model-based methodology for multi-sensor equipment health diagnosis and prognosis , 2007, Eur. J. Oper. Res..
[32] 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.
[33] Andy Liaw,et al. Classification and Regression by randomForest , 2007 .
[34] Dazhong Wu,et al. Fog-Enabled Architecture for Data-Driven Cyber-Manufacturing Systems , 2016 .
[35] Trevor Hastie,et al. The Elements of Statistical Learning , 2001 .
[36] Kai Goebel,et al. Model-Based Prognostics With Concurrent Damage Progression Processes , 2013, IEEE Transactions on Systems, Man, and Cybernetics: Systems.
[37] W. Marsden. I and J , 2012 .