Abstract
The precision of machine tools heavily relies on the motorized spindle’s performance. It is crucial to guarantee the thermal error prediction model’s improved accuracy, as it enables enhanced machining accuracy, reduced errors, and effective control over the prediction model. The northern goshawk optimization (NGO) algorithm is used to optimize the kernel extreme learning machine (KELM) to address thermal error issues. Based on the analysis conducted, the best four temperature measurement locations are chosen. The established thermal error prediction model, combining NGO and KELM, is compared with both the basic KELM model and BA-KELM through comprehensive analysis. The results show that the determination coefficient R2 of NGO-KELM model is 0.132 and 0.026 higher than KELM and BA-KELM. The robustness, stability, and generalization ability of the NGO-KELM modeling method are verified. In order to further reduce the thermal error, this paper puts forward a method of adding a microradiator to the contact between the front and rear bearing chambers and the bearings. The findings demonstrate that it is possible to efficiently lower the temperature at the front and rear bearings and suppress the thermal deformation of the motorized spindle.
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References
Wang HT, Li TM, Wang LP et al (2015) Review on thermal error modeling of machine tools. J Mech Eng 51:119–128
Han Liu K, Wang W, Liu YQ et al (2021) Review on thermal error compensation for axes of CNC machine tools. J Mech Eng 57(156):173
Li Y, Yu ML, Bai YM, Hou ZY et al (2021) A review of thermal error modeling methods for machine tools. J Appl Sci 11(11):52163
Zhou CY, Zhuang LY, Yuan J et al (2018) K-means optimization and experiment of temperature measuring point of machine tool spindle. J Mech Des Manuf 05:41–43+47
Chen S, Wang YQ, Zhi Y et al (2018) Study on optimization of coupling temperature measuring point for spindle thermal elongation modeling. J. Manuf Technol Mach Tools 03:98–102
Guo SJ, Zhang XW, Zhang N et al (2023) Selection of thermal key points of machine tool spindle and prediction of thermal error of typical speed. J Jilin University 53(01):72–81
Zhang L, Chen GH, Zhao DZ et al (2020) Optimization method of temperature measuring point of machine tool spindle based on fuzzy clustering and grey theory. J Mach Tools Hydr 48(22):85–90
Chen YM, Chen PM, Wang Z et al (2022) Simulation analysis of thermal characteristics of KX-1 motorized spindle based on ANSYS. J Electromech Eng Technol 51(10):169–173
Wu CQ, Hua WJ, Zhou Q et al (2018) Thermal characteristics analysis of mechanical spindle. J Mach Tools Hydr 46(23):156–159
Dai Y, Yin XM, Wei WQ et al (2020) Research on thermal error modeling of high-speed motorized spindle based on ANFIS. J Chinese J Sci Inst 41(06):50–58
Xin ZP, Feng XY, Du FX et al (2019) Modeling and analysis of machine tool thermal error based on BP neural network. J Combined Mach Tool Auto Mach Tech 08:39–43
Gaoqiang LI (2021) ZHANG Yu and LI Ming: Study on thermal error modeling method for CNC machine tool based on GA-LSS-VM. J. Mach Tool Hydr 49(2):26–30
Cao L, Peng Y, Yin M et al (2022) Thermal error modeling of spindle in horizontal machining center based on MEA-BP algorithm. J Combined Mach Tool Auto Mach Technol 07:30–3337
Sun A, Wang LS, Xie XL (2022) Thermal error modeling of spindle based on MEA-NARX neural network. J Mach Tools Hydr 50(24):49–53
Wen MF, Zhong JL, Peng BY et al (2022) Optimization modeling of thermal error of motorized spindle based on WOA-SVR. J. Mach Tools Hydr 50(22):38–42
Du LQ, Hu J, Yu YW (2022) Thermal error modeling of machine tools based on chaotic evolution and CNN-GRU. J. Combined Mach Tool Auto Mach Technol 08:18–20+25
Liu JL, Ma C, Gui HQ, Wang S (2021) L: Thermally-induced error compensation of spindle system based on long short term memory neural networks. J Applied Soft Comput J 102:107094
Yang X, Shi X, Mu Y et al (2019) Thermal error compensation model for a motorized spindle with shaft core cooling based on exponential function. J Int J Adv Manuf Technol 103(4805):4813
Peng LQ, Yu HT, Chen C, He QX, Zhang H, Zhao FL, Qin MM, Feng YY, Feng W (2023) Tailoring dense, orientation-tunable, and interleavedly structured carbon-based heat dissipation plates. Adv. Sci 10:2205962
Lu LB, Zhang Z, Guan YC, Zheng HY (2018) Enhancement of heat dissipation by laser micro structuring for LED module. Polymers 10:886
You YX, Zhang BB, Tao SL, Liang ZH, Tang B, Zhou R, Yuan D (2021) Effect of surface microstructure on the heat dissipation performance of heat sinks used in electronic devices. Micromachines 12:265
Zhuang J, Hu W, Fan YQ, Sun JY, He XX, Xu H, Huang Y, Wu DM (2019) Fabrication and testing of metal/polymer microstructure heat exchangers based on micro embossed molding method. Microsyst Technol 25:381–388
Ventola L, Chiavazzo E, Calignano F, Manfredi D, Asinari P (2014) Heat transfer enhancement by finned heat sinks with micro-structured roughness. J Phys : Conf Series 494:012009
JJ Cheng, FX Wei, SY Chiam (2020) Electrodeposited copper micropillar surfaces with pulse reverse voltammetry for enhanced heat dissipation. ACS Appl Electron Mater 2(4):1041–1047
Dai Y, Yin XM, Wei WQ, Wang G, Zhan SQ (2020) Thermal error modeling of high-speed motorized spindle based on ANFIS. J Chinese J Sci Inst 41(6):50–58
Dehghani M, Hubalovsky S, Trojovsky P (2021) Northern goshawk optimization: a new swarm-based algorithm for solving optimization problems. M EEE Access 9:162059–162080
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This work was supported by the Supported by Opening Project of the Key Laboratory of Advanced Manufacturing and Intelligent Technology (Ministry of Education) (grant number: KFKT202204) and National Natural Science Foundation of China (grant number: 52075134).
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Zhaolong, L., Benchao, S., Wenming, Z. et al. Thermal error modeling of motorized spindle and application of miniature radiator in motorized spindle. Int J Adv Manuf Technol 131, 1107–1118 (2024). https://doi.org/10.1007/s00170-024-13149-y
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DOI: https://doi.org/10.1007/s00170-024-13149-y