Thermal error prediction of precision boring machine tools based on extreme gradient boosting algorithm-improved sailed fish optimizer-bi-directional ordered neurons-long short-term memory neural network model and physical-edge-cloud system
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[1]
A.M. Abdulshahed, A.P. Longstaff, S. Fletcher, A. Potdar, Thermal error modelling of a gantry-type 5-axis machine tool using a grey neural network model, J. Manuf. Syst. 41 (2016) 130–142,.
[2]
M.S. Acar, O. Erbas, O. Arslan, The performance of vapor compression cooling system aided Ranque-Hilsch vortex tube, Therm. Sci. 23 (2017) 1189–1201,.
[3]
A. Bello Sururah, O. Oyedele Lukumon, Olugbenga O. Akinade, Muhammad Bilal, Juan Manuel Davila Delgado, Lukman A. Akanbi, Anuoluwapo O. Ajayi, Hakeem A. Owolabi, Cloud computing in construction industry: use cases, benefits and challenges, Autom. ConStruct. 122 (2021),. 103441.
[4]
T.Q. Chen, C. Guestrin, XGBoost: a scalable tree boosting system, Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining 8 (2016) 785–794,.
[5]
C. Chen, H. Shi, Z.W. Jiang, A. Salhi, R.X. Chen, R.X. Cui, B. Yu, Dnn-Dtis, Improved drug-target interactions prediction using XGBoost feature selection and deep neural network, Comput. Biol. Med. 136 (2021),.
[6]
Y. Dai, J. Pang, X.K. Rui, W.W. Li, Q.H. Wang, S.K. Li, Thermal error prediction model of high-speed motorized spindle based on DELM network optimized by weighted mean of vectors algorithm, Case Stud. Therm. Eng. 47 (2023),.
[7]
De Araujo, Lins, Paulo Ricardo Marques de Araujo, Romulo Gonçalves Lins, Cloud-based approach for automatic CNC workpiece origin localization based on image analysis, Robot. Comput.-Integr. Manuf. 68 (2021) (2021),.
[8]
X.L. Deng, H. Lin, J.C. Wang, C.X. Xie, J.Z. Fu, Review on thermal design of machine tool spindles, Opt Precis. Eng. 26 (6) (2018) 1415–1429,.
[9]
S. El Kafhali, K. Salah, Performance modelling and analysis of Internet of Things enabled healthcare monitoring systems, IET Netw. 8 (1) (2019) 48–58,.
[10]
M. Fujishima, K. Narimatsu, N. Irino, Y. Ido, Thermal displacement reduction and compensation of a turning center, CIRP J. Manuf. Sci. Technol. 22 (2018) 111–115,.
[11]
S.N. Grama, A. Mathur, R. Aralaguppi, T. Subramanian, Optimization of high speed machine tool spindle to minimize thermal distortion, Proc. CIRP 58 (2017) 457–462,.
[12]
H.Q. Gui, J.L. Liu, C. Ma, M.Y. Li, S.L. Wang, New machine learning application platform for spatial–temporal thermal error prediction and control with STFGCN for ball screw system, Mech. Syst. Signal Process. 192 (1) (2023),.
[13]
M.T. Hoang, Y.R. Wu, Error compensation method for milling single-threaded screw rotors with end mill tools, Mech. Mach. Theor. 157 (2021),.
[14]
C.G. Huang, H.Z. Huang, Y.F. Li, A bidirectional LSTM prognostics method under multiple operational conditions, IEEE Trans. Ind. Electron. 66 (11) (2019) 8792–8802,.
[15]
Y.B. Huang, K.C. Fan, Z.F. Lou, W. Sun, A novel modeling of volumetric errors of three-axis machine tools based on Abbe and Bryan principles, Int. J. Mach. Tool Manufact. 151 (2020),.
[16]
G.J. Jia, X. Zhang, X.Z. Wang, X.P. Zhang, N.D. Huang, A spindle thermal error modeling based on 1DCNN-GRU-Attention architecture under controlled ambient temperature and active cooling, Int. J. Adv. Manuf. Technol. 127 (2023) 1525–1539,.
[17]
Y. Li, J. Zhao, S.J. Ji, Thermal positioning error modeling of machine tools using a bat algorithm-based back propagation neural network, Int. J. Adv. Manuf. Technol. 97 (2018) 2575–2586,.
[18]
J.Q. Li, Y.F. Zhang, Z.Z. Chen, J. Wang, M. Fang, C.W. Luo, H.H. Wang, A novel edge-enabled SLAM solution using projected depth image information, Neural Comput. Appl. 32 (2020) 15369–15381,.
[19]
Y.C. Liang, W.D. Li, P. Lou, J.M. Hu, Thermal error prediction for heavy-duty CNC machines enabled by long short-term memory networks and fog-cloud architecture, J. Manuf. Syst. 62 (2022) 950–963,.
[20]
J.L. Liu, C. Ma, S.L. Wang, Data-driven thermally-induced error compensation method of high-speed and precision five-axis machine tools, Mech. Syst. Signal Process. 138 (2020),.
[21]
J.L. Liu, C. Ma, H.Q. Gui, S.L. Wang, Thermally-induced error compensation of spindle system based on long short term memory neural networks, Appl. Soft Comput. 102 (2021),.
[22]
P.L. Liu, Z.C. Du, H.M. Li, M. Deng, X.B. Feng, J.G. Yang, Thermal error modeling based on BiLSTM deep learning for CNC machine tool, Adv. Manuf. 9 (2021) 235–249,.
[23]
W. Long, J.J. Jiao, X.M. Liang, M.Z. Tang, An exploration-enhanced grey wolf optimizer to solve high-dimensional numerical optimization, Eng. Appl. Artif. Intell. 68 (2018) 63–80,.
[24]
H.D. Luo, H.M. Cai, H. Yu, Y. Sun, Z.M. Bi, L.H. Jiang, A short-term energy prediction system based on edge computing for smart city, Future Generat. Comput. Syst. 101 (2019) 444–457,.
[25]
E. Miao, Y. Liu, H. Liu, Z.H. Gao, W. Li, Study on the effects of changes in temperature-sensitive points on thermal error compensation model for CNC machine tool, Int. J. Mach. Tool Manufact. 97 (2015) 50–59,.
[26]
H.V. Ngoc, J.R.R. Mayer, E. Bitar-Nehme, Deep learning LSTM for predicting thermally induced geometric errors using rotary axes' powers as input parameters, CIRP J. Manuf. Sci. Technol. 37 (2022) 70–80,.
[27]
P. Pinson, G. Kariniotakis, Conditional prediction intervals of wind power generation, IEEE Trans. Power Syst. 25 (4) (2010) 1845–1856,.
[28]
M. Pirtini, I. Lazoglu, Forces and hole quality in drilling, Int. J. Mach. Tool Manufact. 45 (11) (2005) 1271–1281,.
[29]
R. Ramesh, M.A. Mannan, A.N. Poo, Error compensation in machine tools—a review: Part II: thermal errors, Int. J. Mach. Tool Manufact. 40 (9) (2000) 1257–1284,.
[30]
Z. Said, P. Sharma, B.J. Bora, A.K. Pandey, Sonication impact on thermal conductivity of f-MWCNT nanofluids using XGBoost and Gaussian process regression, J. Taiwan Inst. Chem. Eng. 145 (2023),.
[31]
S. Shadravan, H.R. Naji, V.K. Bardsiri, The Sailfish Optimizer: a novel nature-inspired metaheuristic algorithm for solving constrained engineering optimization problems, Eng. Appl. Artif. Intell. 80 (2019) 20–34,.
[32]
P. Sharma, B.J. Bora, A review of modern machine learning techniques in the prediction of remaining useful life of lithium-ion batteries, Batteries 9 (1) (2023) 13,.
[33]
H. Shi, B. He, Y.Y. Yue, C.Q. Min, X.S. Mei, Cooling effect and temperature regulation of oil cooling system for ball screw feed drive system of precision machine tool, Appl. Therm. Eng. 161 (2019),.
[34]
F. Tan, M. Yin, L. Wang, G.F. Yin, Spindle thermal error robust modeling using LASSO and LS-SVM, Int. J. Adv. Manuf. Technol. 94 (2018) 2861–2874,.
[35]
Z.H. Tang, G.N. Zhao, T.H. Ouyang, Two-phase deep learning model for short-term wind direction forecasting, Renew. Energy 173 (2021) 1005–1016,.
[36]
V.V. Wanatasanappan, P.K. Kanti, p. Sharma, N. Husna, M.Z. Abdullah, Viscosity and rheological behavior of Al2O3-Fe2O3/water-EG based hybrid nanofluid: a new correlation based on mixture ratio, J. Mol. Liq. 375 (2023),.
[37]
X.Y. Wei, H.H. Ye, J.H. Zhou, S.J. Pan, M.Y. Qian, A regularized regression thermal error modeling method for CNC machine tools under different ambient temperatures and spindle speeds, Sensors 23 (10) (2023) 4916,.
[38]
X.Y. Wu, S. Zhang, W.D. Xiao, Y.X. Yin, The exploration/exploitation tradeoff in whale optimization algorithm, IEEE Access 7 (2019) 125919–125928,.
[39]
C.Y. Wu, S.T. Xiang, W.S. Xiang, Spindle thermal error prediction approach based on thermal infrared images: a deep learning method, J. Manuf. Syst. 59 (2021) 67–80,.
[40]
C.J. Xia, S.L. Wang, C. Ma, S.B. Wang, Y.L. Xiao, Crucial geometric error compensation towards gear grinding accuracy enhancement based on simplified actual inverse kinematic model, Int. J. Mech. Sci. 169 (2020),.
[41]
J.Y. Yan, J.G. Yang, Application of synthetic grey correlation theory on thermal point optimization for machine tool thermal error compensation, Int. J. Adv. Manuf. Technol. 43 (2009) 1124–1132,.
[42]
X.A. Yan, Y. Liu, Y.D. Xu, M.P. Jia, Multistep forecasting for diurnal wind speed based on hybrid deep learning model with improved singular spectrum decomposition, Energy Convers. Manag. 225 (2020),.
[43]
S.H. Yang, K.H. Kim, Y.K. Park, Measurement of spindle thermal errors in machine tool using hemispherical ball bar test, Int. J. Mach. Tool Manufact. 44 (2–3) (2004) 333–340,.
[44]
J. Yang, H. Shi, B. Feng, L. Zhao, C. Ma, X.S. Mei, Thermal error modeling and compensation for a high-speed motorized spindle, Int. J. Adv. Manuf. Technol. 77 (2015) 1005–1017,.
[45]
J.C. Yang, J.B. Wen, Y.H. Wang, B. Jiang, H.H. Wang, H.B. Song, Fog-Based marine environmental information monitoring toward ocean of Things, IEEE Internet Things J. 7 (5) (2019) 4238–4247,.
[46]
Q. Yuan, C. Ma, J.L. Liu, H.Q. Gui, M.Y. Li, S.L. Wang, Correlation analysis-based thermal error control with ITSA-GRU-A model and cloud-edge-physical collaboration framework, Adv. Eng. Inf. 54 (2022),.
[47]
M.A. Zamora-Izquierdo, J. Santa, J.A. Martínez, V. Martínez, A.F. Skarmeta, Smart farming IoT platform based on edge and cloud computing, Biosyst. Eng. 177 (2019) 4–17,.
[48]
S. Zeng, C. Ma, J.L. Liu, M.Y. Li, H.Q. Gui, Sequence-to-sequence based LSTM network modeling and its application in thermal error control framework, Appl. Soft Comput. 138 (2023),.
[49]
Y. Zhang, R.R. Zhang, Q.F. Ma, Y.H. Wang, Q.Q. Wang, Z.H. Huang, L.Y. Huang, A feature selection and multi-model fusion-based approach of predicting air quality, ISA Trans. 100 (2020) 210–220,.
[50]
L. Zhang, J.L. Liu, C. Ma, H.Q. Gui, Intelligent integrated framework towards high-accuracy machining, Eng. Sci. Technol. 40 (2023),.
[51]
L. Zhang, C. Ma, J.L. Liu, H.Q. Gui, S.L. Wang, Implementation of precision machine tool thermal error compensation in edge-cloud-fog computing architecture, J. Manuf. Sci. Eng.-Trans. ASME. 145 (7) (2023),.
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Published: 01 February 2024
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