Energy Efficiency Modeling for Configuration-Dependent Machining via Machine Learning: A Comparative Study

[1]  Erik Cambria,et al.  Recent Trends in Deep Learning Based Natural Language Processing , 2017, IEEE Comput. Intell. Mag..

[2]  Hakkı Özgür Ünver,et al.  Modelling and optimization of energy consumption for feature based milling , 2016 .

[3]  Wei Cai,et al.  Optimisation of cutting parameters for improving energy efficiency in machining process , 2019, Robotics Comput. Integr. Manuf..

[4]  Ying Tang,et al.  Meta-Reinforcement Learning of Machining Parameters for Energy-Efficient Process Control of Flexible Turning Operations , 2021, IEEE Transactions on Automation Science and Engineering.

[5]  Nora El-Gohary,et al.  A review of data-driven building energy consumption prediction studies , 2018 .

[6]  Nian Zhang,et al.  An effective LS-SVM-based approach for surface roughness prediction in machined surfaces , 2016, Neurocomputing.

[7]  Jiujun Cheng,et al.  Dendritic Neuron Model With Effective Learning Algorithms for Classification, Approximation, and Prediction , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[8]  MengChu Zhou,et al.  Modeling and Planning for Dual-Objective Selective Disassembly Using and/or Graph and Discrete Artificial Bee Colony , 2019, IEEE Transactions on Industrial Informatics.

[9]  Gilson A. Giraldi,et al.  Convolutional Neural Network approaches to granite tiles classification , 2017, Expert Syst. Appl..

[10]  Weiming Shen,et al.  A sensor fusion and support vector machine based approach for recognition of complex machining conditions , 2018, J. Intell. Manuf..

[11]  Can Cui,et al.  A recommendation system for meta-modeling: A meta-learning based approach , 2016, Expert Syst. Appl..

[12]  Xin Ma,et al.  A novel fractional time delayed grey model with Grey Wolf Optimizer and its applications in forecasting the natural gas and coal consumption in Chongqing China , 2019, Energy.

[13]  Erry Yulian Triblas Adesta,et al.  Energy cost modeling for high speed hard turning , 2011 .

[14]  Jae-Hun Kim,et al.  Deep Convolutional Neural Networks for Predominant Instrument Recognition in Polyphonic Music , 2016, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

[15]  Andrew D. Ball,et al.  An approach to fault diagnosis of reciprocating compressor valves using Teager-Kaiser energy operator and deep belief networks , 2014, Expert Syst. Appl..

[16]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[17]  Youlun Xiong,et al.  A novel approach to fixture design on suppressing machining vibration of flexible workpiece , 2012 .

[18]  Li Li,et al.  A knowledge-driven method of adaptively optimizing process parameters for energy efficient turning , 2019, Energy.

[19]  Jin Young Choi,et al.  Action-Driven Visual Object Tracking With Deep Reinforcement Learning , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[20]  Xin Ma,et al.  A brief introduction to the Grey Machine Learning , 2018, ArXiv.

[21]  Congbo Li,et al.  An Internet of Things based energy efficiency monitoring and management system for machining workshop , 2018, Journal of Cleaner Production.

[22]  Trung-Thanh Nguyen,et al.  Prediction and optimization of machining energy, surface roughness, and production rate in SKD61 milling , 2019, Measurement.

[23]  Jingxiang Lv,et al.  Energy-cyber-physical system enabled management for energy-intensive manufacturing industries , 2019, Journal of Cleaner Production.

[24]  Andrés Bustillo,et al.  Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth , 2017, Journal of Intelligent Manufacturing.

[25]  Oscar Velásquez Arriaza,et al.  Trade-off analysis between machining time and energy consumption in impeller NC machining , 2017 .

[26]  MengChu Zhou,et al.  TL-GDBN: Growing Deep Belief Network With Transfer Learning , 2019, IEEE Transactions on Automation Science and Engineering.

[27]  Jiguo Yu,et al.  An XGBoost-based physical fitness evaluation model using advanced feature selection and Bayesian hyper-parameter optimization for wearable running monitoring , 2019, Comput. Networks.

[28]  Adem Çiçek,et al.  ANN and multiple regression method-based modelling of cutting forces in orthogonal machining of AISI 316L stainless steel , 2014, Neural Computing and Applications.

[29]  Girish Kant,et al.  Predictive Modelling for Energy Consumption in Machining Using Artificial Neural Network , 2015 .

[30]  Ning Li,et al.  Gaussian process regression for tool wear prediction , 2018 .

[31]  Zhen Zhang,et al.  Integrated ANN-LWPA for cutting parameter optimization in WEDM , 2015 .

[32]  Z. M. Bi,et al.  Energy Modeling of Machine Tools for Optimization of Machine Setups , 2012, IEEE Transactions on Automation Science and Engineering.

[33]  Jeffrey A. Fessler,et al.  Asymptotic performance of PCA for high-dimensional heteroscedastic data , 2017, J. Multivar. Anal..

[34]  Konrad Wegener,et al.  Methods for evaluation of energy efficiency of machine tools , 2015 .

[35]  Lifeng Wu,et al.  Daily reference evapotranspiration prediction based on hybridized extreme learning machine model with bio-inspired optimization algorithms: Application in contrasting climates of China , 2019, Journal of Hydrology.

[36]  Hui Liu,et al.  Robust modeling method for thermal error of CNC machine tools based on ridge regression algorithm , 2017 .

[37]  Ali M. Abdulshahed,et al.  Thermal error modelling of a gantry-type 5-axis machine tool using a Grey Neural Network Model , 2016 .

[38]  Xiang Li,et al.  Remaining useful life estimation in prognostics using deep convolution neural networks , 2018, Reliab. Eng. Syst. Saf..

[39]  Teng Liu,et al.  Power matching based dissipation strategy onto spindle heat generations , 2017 .

[40]  MengChu Zhou,et al.  Disassembly Sequence Planning Considering Fuzzy Component Quality and Varying Operational Cost , 2018, IEEE Transactions on Automation Science and Engineering.

[41]  Moneer Helu,et al.  Towards a generalized energy prediction model for machine tools. , 2017, Journal of manufacturing science and engineering.

[42]  Fei Liu,et al.  Key performance indicators for assessing inherent energy performance of machine tools in industries , 2018, Int. J. Prod. Res..

[43]  Guillem Quintana,et al.  Modelling Power Consumption in Ball-End Milling Operations , 2011 .

[44]  Miran Brezocnik,et al.  A comparison of machine learning methods for cutting parameters prediction in high speed turning process , 2016, Journal of Intelligent Manufacturing.

[45]  Jiajun Wang,et al.  Parameter optimization of interval Type-2 fuzzy neural networks based on PSO and BBBC methods , 2019, IEEE/CAA Journal of Automatica Sinica.

[46]  Carlos Pardo,et al.  A machine-learning based solution for chatter prediction in heavy-duty milling machines , 2018, Measurement.

[47]  Umberto Berardi,et al.  Day-ahead prediction of hourly electric demand in non-stationary operated commercial buildings: A clustering-based hybrid approach , 2017 .

[48]  Robert X. Gao,et al.  Deep learning and its applications to machine health monitoring , 2019, Mechanical Systems and Signal Processing.