A two-step machine learning approach for dynamic model selection: A case study on a micro milling process
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[1] Saeed Ansari Rad,et al. Back-stepping control of delta parallel robots with smart dynamic model selection for construction applications , 2022, Automation in Construction.
[2] Santhosh Madasthu,et al. A novel reinforced online model selection using Q-learning technique for wind speed prediction , 2022, Sustainable Energy Technologies and Assessments.
[3] Lilan Liu,et al. Digital twin-driven surface roughness prediction and process parameter adaptive optimization , 2022, Adv. Eng. Informatics.
[4] S. Wojciechowski. Estimation of Minimum Uncut Chip Thickness during Precision and Micro-Machining Processes of Various Materials—A Critical Review , 2021, Materials.
[5] Neha Khatri,et al. Monitoring and Predicting the Surface Generation and Surface Roughness in Ultraprecision Machining: A Critical Review , 2021, Machines.
[6] Gerardo Beruvides,et al. Ensemble of convolutional neural networks based on an evolutionary algorithm applied to an industrial welding process , 2021, Comput. Ind..
[7] Abbas S. Milani,et al. A machine learning framework with dataset-knowledgeability pre-assessment and a local decision-boundary crispness score: An industry 4.0-based case study on composite autoclave manufacturing , 2021, Comput. Ind..
[8] Ramon Quiza,et al. Optimization of the Cutting Regime in the Turning of the AISI 316L Steel for Biomedical Purposes Based on the Initial Progression of Tool Wear , 2021, Metals.
[9] Qiuchang Chen,et al. Visual measurement of milling surface roughness based on Xception model with convolutional neural network , 2021, Measurement.
[10] Li Yang,et al. Investigation on the size effect in micro end milling considering the cutting edge radius and the workpiece material , 2021 .
[11] Dongdong Kong,et al. Surface roughness prediction using kernel locality preserving projection and Bayesian linear regression , 2021 .
[12] Jianhua Ma,et al. Variational LSTM Enhanced Anomaly Detection for Industrial Big Data , 2021, IEEE Transactions on Industrial Informatics.
[13] Shanlin Yang,et al. Data-driven decision model based on dynamical classifier selection , 2020, Knowl. Based Syst..
[14] Xin Gao,et al. An ensemble imbalanced classification method based on model dynamic selection driven by data partition hybrid sampling , 2020, Expert Syst. Appl..
[15] Xuemin Shen,et al. Secure and Efficient k NN Classification for Industrial Internet of Things , 2020, IEEE Internet of Things Journal.
[16] Mustafa Kuntoğlu,et al. Investigation of signal behaviors for sensor fusion with tool condition monitoring system in turning , 2020 .
[17] Gerardo Beruvides,et al. Computer Vision System for Welding Inspection of Liquefied Petroleum Gas Pressure Vessels Based on Combined Digital Image Processing and Deep Learning Techniques , 2020, Sensors.
[18] Xiang Li,et al. A virtual metrology method with prediction uncertainty based on Gaussian process for chemical mechanical planarization , 2020, Comput. Ind..
[19] Seokgoo Kim,et al. Ranked Feature-Based Laser Material Processing Monitoring and Defect Diagnosis Using k-NN and SVM , 2020 .
[20] P. Sam Paul,et al. A neural network model to predict surface roughness during turning of hardened SS410 steel , 2020, Int. J. Syst. Assur. Eng. Manag..
[21] Atsushi Uchida,et al. Adaptive model selection in photonic reservoir computing by reinforcement learning , 2020, Scientific Reports.
[22] Zhongxiao Peng,et al. Model-based surface roughness estimation using acoustic emission signals , 2020 .
[23] Jie Zhang,et al. Reinforced Deterministic and Probabilistic Load Forecasting via $Q$ -Learning Dynamic Model Selection , 2020, IEEE Transactions on Smart Grid.
[24] Dabin Zhang,et al. Forecasting Agricultural Commodity Prices Using Model Selection Framework With Time Series Features and Forecast Horizons , 2020, IEEE Access.
[25] Gerardo Beruvides,et al. Cloud-Based Industrial Cyber–Physical System for Data-Driven Reasoning: A Review and Use Case on an Industry 4.0 Pilot Line , 2020, IEEE Transactions on Industrial Informatics.
[26] N. Rajesh Jesudoss Hynes,et al. Prediction and optimization of surface roughness in thermal drilling using integrated ANFIS and GA approach , 2020 .
[27] Chunhui Zhao,et al. Enhanced Random Forest With Concurrent Analysis of Static and Dynamic Nodes for Industrial Fault Classification , 2020, IEEE Transactions on Industrial Informatics.
[28] Rodolfo E. Haber,et al. Quality monitoring of complex manufacturing systems on the basis of model driven approach , 2020 .
[29] Ulisses Braga-Neto,et al. Model Selection for Classification , 2020 .
[30] Tianbiao Yu,et al. Surface generation modeling of micro milling process with stochastic tool wear , 2020 .
[31] Xiaofeng Yuan,et al. A two‐layer ensemble learning framework for data‐driven soft sensor of the diesel attributes in an industrial hydrocracking process , 2019, Journal of Chemometrics.
[32] Thyago P. Carvalho,et al. A systematic literature review of machine learning methods applied to predictive maintenance , 2019, Comput. Ind. Eng..
[33] M. Silva,et al. Investigation of burr formation and tool wear in micromilling operation of duplex stainless steel , 2019, Precision Engineering.
[34] Zhiwen Yu,et al. A survey on ensemble learning , 2019, Frontiers of Computer Science.
[35] Hojjat Adeli,et al. A dynamic ensemble learning algorithm for neural networks , 2019, Neural Computing and Applications.
[36] Farough Agin,et al. Application of decision tree, artificial neural networks, and adaptive neuro-fuzzy inference system on predicting lost circulation: A case study from Marun oil field , 2019, Journal of Petroleum Science and Engineering.
[37] J. Seume,et al. Surface roughness of real operationally used compressor blade and blisk , 2019, Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering.
[38] Prabhakar Kudva,et al. Dynamic Autoselection and Autotuning of Machine Learning Models for Cloud Network Analytics , 2019, IEEE Transactions on Parallel and Distributed Systems.
[39] Matteo Lancini,et al. Characterization of machine tools and measurement system for micromilling , 2019, Nanotechnology and Precision Engineering.
[40] E. Wagenmakers,et al. Limitations of Bayesian Leave-One-Out Cross-Validation for Model Selection , 2018, Computational brain & behavior.
[41] Gordon P. Warn,et al. A method for model selection using reinforcement learning when viewing design as a sequential decision process , 2018, Structural and Multidisciplinary Optimization.
[42] Luís Torgo,et al. Arbitrage of forecasting experts , 2018, Machine Learning.
[43] Weiming Shen,et al. A sensor fusion and support vector machine based approach for recognition of complex machining conditions , 2018, J. Intell. Manuf..
[44] Xiao Liu,et al. Silicon Carbide Surface Quality Prediction Based on Artificial Intelligence Methods on Multi-sensor Fusion Detection Test Platform , 2018, Machining Science and Technology.
[45] Jie Ding,et al. Model Selection Techniques: An Overview , 2018, IEEE Signal Processing Magazine.
[46] Minfang Peng,et al. Ultra-Short-Term Wind Power Prediction Based on Multivariate Phase Space Reconstruction and Multivariate Linear Regression , 2018, Energies.
[47] E. García Plaza,et al. Analysis of cutting force signals by wavelet packet transform for surface roughness monitoring in CNC turning , 2018 .
[48] Teng Wang,et al. Real-time monitoring of high-power disk laser welding based on support vector machine , 2018, Comput. Ind..
[49] Christopher J. Taylor,et al. Control of deviations and prediction of surface roughness from micro machining of THz waveguides using acoustic emission signals , 2017 .
[50] Fernando Castaño,et al. Characterization of tool-workpiece contact during the micromachining of conductive materials , 2017 .
[51] Gerardo Beruvides,et al. Coping with Complexity When Predicting Surface Roughness in Milling Processes: Hybrid Incremental Model with Optimal Parametrization , 2017, Complex..
[52] S. Bukkapatnam,et al. Built-up-edge effects on surface deterioration in micromilling processes , 2016 .
[53] Samad Nadimi Bavil Oliaei,et al. Investigating the influence of built-up edge on forces and surface roughness in micro scale orthogonal machining of titanium alloy Ti6Al4V , 2016 .
[54] Mikolaj Kuzinovski,et al. Development of mathematical models for surface roughness parameter prediction in turning depending on the process condition , 2016 .
[55] Gerardo Beruvides,et al. Surface roughness modeling and optimization of tungsten–copper alloys in micro-milling processes , 2016 .
[56] Mohsen Marani Barzani,et al. Fuzzy logic based model for predicting surface roughness of machined Al–Si–Cu–Fe die casting alloy using different additives-turning , 2015 .
[57] João Paulo Davim,et al. State of the Art on Micromilling of Materials, a Review , 2012 .
[58] Rodolfo E. Haber,et al. A Transductive Neuro-Fuzzy Controller: Application to a Drilling Process , 2010, IEEE Transactions on Neural Networks.
[59] Paul Mativenga,et al. Size effect and tool geometry in micromilling of tool steel , 2009 .
[60] Rodolfo E. Haber,et al. A classic solution for the control of a high-performance drilling process , 2007 .
[61] M. Elbestawi,et al. Surface defects during microcutting , 2006 .
[62] Iván Rodríguez,et al. Fuzzy control of a multiple hearth furnace , 2004, Comput. Ind..
[63] Clodeinir Ronei Peres,et al. Fuzzy model and hierarchical fuzzy control integration: an approach for milling process optimization , 1999 .