RESEARCH PAPER
Thermal error modeling of spindle and dynamic machining accuracy reliability analysis of CNC machine tools based on IA and LHSMC
| 1 | Shanghai Maritime University, Logistics Engineering College, Shanghai, 201306, China |
| 2 | Qiqihar Second Machine Tool (Group) Co.Ltd., Heilongjiang, 161005, China |
| 3 | Beijing Union University, College of Robotics, Beijing, 100027, China |
Publication date: 2022-03-31
Eksploatacja i Niezawodność – Maintenance and Reliability 2022;24(1):100–113
HIGHLIGHTS
- A thermal error model of spindle unit is developed based on IA.
- A machining accuracy model considering the thermal error is constructed based on MBS.
- The machining accuracy reliability analysis method is presented based on LHSMC.
- The effectiveness of the method is verified by a four-axis machine tool.
KEYWORDS
electric spindle unitthermal errorlatin hypercube sampling monte carlo methodfiniteelement simulationmachining accuracy reliability
ABSTRACT
Machining accuracy reliability as a key index of CNC machine tools is seriously influenced by the geometric and thermal errors. In the paper, a spindle unit thermal error modeling and machining accuracy reliability analysis method is proposed. By analyzing the heat generation mechanism, a thermal error model was developed to describe the thermal deformation of the electric spindle. Based on the immune algorithm (IA), the heat generation power and the heat transfer coefficient were optimized, and the thermal error was obtained by finite element thermal-mechanical coupling. By adopting the multi-body system theory (MBS), a dynamic machining accuracy model was put forward including the geometric and thermal errors. Based on the Latin hypercube sampling Monte Carlo method (LHSMC), a machining accuracy reliability analysis method was proposed to characterize the machining accuracy reliability considering the geometric and thermal errors. The method was employed to a machine tool, and the experimental results indicate the verification and superiority of the method.
REFERENCES (34)
1.
Cai LG, Zhang ZL, Cheng Q, Liu ZF, Gu PH. A geometric accuracy design method of multi-axis NC machine tool for improving machining accuracy reliability. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2014; 17(1): 143–155, https://doi.org/10.17531/ein.2....
2.
Cheng Q, Zhao HW, Zhao YS, Sun BW, Gu PH. Machining accuracy reliability analysis of multi-axis machine tool based on Monte Carlo simulation. Journal of Intelligent Manufacturing 2018; 29(1): 191–209, https://doi.org/10.1007/s10845....
3.
Chen JX, Lin SW, Zhou XL. A comprehensive error analysis method for the geometric error of multi-axis machine tool. International Journal of Machine Tools and Manufacture 2016; 106: 56–66, https://doi.org/10.1016/j.ijma....
4.
Cheng Q, Sun BW, Zhao YS, Gu PH. A method to analyze the machining accuracy reliability sensitivity of machine tools based on Fast Markov Chain simulation. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2016; 18(4): 552–564, https://doi.org/10.17531/ein.2....
5.
Fu GQ, Fu JZ, Xu YT, Chen ZC, Lai JT. Accuracy enhancement of five-axis machine tool based on differential motion matrix: Geometric error modeling, identification and compensation. International Journal of Machine Tools and Manufacture 2015; 89: 170–181, https://doi.org/10.1016/j.ijma....
6.
Hou RS, Yan ZZ, Du HY, Chen T, Tao T, Mei XS. The Application of Multi-objective Genetic Algorithm in the Modeling of Thermal Error of NC Lathe. Procedia CIRP 2018; 67: 332–337, https://doi.org/10.1016/j.proc....
7.
Huang YB, Fan KC, Lou ZF, Sun W. A novel modeling of volumetric errors of three-axis machine tools based on Abbe and Bryan principles. International Journal of Machine Tools and Manufacture 2020; 151: 103527, https://doi.org/10.1016/j.ijma....
8.
Jiang ZY, Huang XZ, Chang MX, Li C, Ge Y. Thermal error prediction and reliability sensitivity analysis of motorized spindle based on Kriging model. Engineering Failure Analysis 2021; 127: 105558, https://doi.org/10.1016/j.engf....
9.
Kiridena V, Ferreira PM. Mapping the effects of positioning errors on the volumetric accuracy of five-axis CNC machine tools. International Journal of Machine Tools and Manufacture 1993; 33(3): 417–437, https://doi.org/10.1016/0890-6....
10.
Kwintarini W, Wibowo A, Martawirya YY. Mathematical approach for geometric error modeling of three axis CNC vertical milling machine. Applied Mechanics and Materials 2016; 842: 303–310, https://doi.org/10.4028/www.sc....
11.
Li B, Tian XT, Zhang M. Thermal error modeling of machine tool spindle based on the improved algorithm optimized BP neural network. The International Journal of Advanced Manufacturing Technology 2019; 105(1–4): 1497–1505, https://doi.org/10.1007/s00170....
12.
Li HZ, Yang ZJ, Xu BB, Chen CH, Kan YN, Liu GF. Reliability Evaluation of NC Machine Tools considering Working Conditions. Mathematical Problems in Engineering 2016; 2016: 1–11, https://doi.org/10.1155/2016/9....
13.
Li YF, Zhang YJ, Zhao YQ, Shi XJ. Thermal-mechanical coupling calculation method for deformation error of motorized spindle of machine tool. Engineering Failure Analysis 2021; 128: 105597, https://doi.org/10.1016/j.engf....
14.
Liu K, Sun MJ, Zhu TJ, Wu YL, Liu Y. Modeling and compensation for spindle’s radial thermal drift error on a vertical machining center. International Journal of Machine Tools and Manufacture 2016; 105: 58–67, https://doi.org/10.1016/j.ijma....
15.
Liu K, Wu JK, Liu HB, Sun MJ, Wang YQ. Reliability analysis of thermal error model based on DBN and Monte Carlo method. Mechanical Systems and Signal Processing 2021; 146: 107020, https://doi.org/10.1016/j.ymss....
16.
Liu T. Thermal error modeling and active control method of electric spindle unit. Tianjin: Tianjin University 2016.
17.
Luo X, Xie FG, Liu XJ, Li J. Error modeling and sensitivity analysis of a novel 5-degree-of-freedom parallel kinematic machine tool. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 2019; 233(6): 1637–1652, https://doi.org/10.1177/095440....
18.
Ma C, Yang J, Zhao L, Mei XS, Shi, H. Simulation and experimental study on the thermally induced deformations of high-speed spindle system. Applied Thermal Engineering 2015; 86: 251–268, https://doi.org/10.1016/j.appl....
19.
Mohamed R, Fethi S, Abdelmadjid C. Study and Modeling of Machining Errors on the NC Machine Tool. International Journal of Mechanical Engineering and Robotics Research 2017; 6(1): 54–57, https://doi.org/10.18178/ijmer....
20.
Shi H, Jiang CP, Yan ZZ, Tao T, Mei XS. Bayesian neural network–based thermal error modeling of feed drive system of CNC machine tool. International Journal of Advanced Manufacturing Technology 2020; 108(9–10): 3031–3044, https://doi.org/10.1007/s00170....
21.
Srivastava AK, Veldhuis SC, Elbestawit MA. Modelling geometric and thermal errors in a five-axis CNC machine tool. International Journal of Machine Tools and Manufacture 1995; 35(9): 1321–1337, https://doi.org/10.1016/0890-6....
22.
Uhlmann E, Hu J. Thermal Modelling of a High-Speed Motor Spindle. Procedia CIRP 2012; 1(1): 313–318, https://doi.org/10.1016/j.proc....
23.
Wang ZM, Yuan H. Enhancing machining accuracy reliability of multi-axis CNC machine tools using an advanced importance sampling method. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2021; 23(3): 559–568, https://doi.org/10.17531/ein.2....
24.
Xiang ST, Yao XD, Du ZC, Yang JG. Dynamic linearization modeling approach for spindle thermal errors of machine tools. Mechatronics 2018; 53: 215–228, https://doi.org/10.1016/j.mech....
25.
Yang AS, Yu XH, Zhuang JR, Lee CY, Hsieh WH. DOE-FEM based design improvement to minimize thermal errors of a high-speed spindle system. Thermal Science and Engineering Progress 2018; 8: 525–536, https://doi.org/10.1016/j.tsep....
26.
Zhang ZL, Cai LG, Cheng Q, Lui ZF, Gu PH. A geometric error budget method to improve machining accuracy reliability of multi-axis machine tools. Journal of Intelligent Manufacturing 2019; 30(2): 495–519, https://doi.org/10.1007/s10845....
27.
Zhang ZL, Cheng Q, Qi BB, Tao ZQ. A general approach for the machining quality evaluation of S-shaped specimen based on POS-SQP algorithm and Monte Carlo method. Journal of Manufacturing Systems, 2021, 60: 553-568, https://doi.org/10.1016/j.jmsy....
28.
Zhang ZL, Qi Y, Cheng Q, Liu ZF, Tao ZQ, Cai LG. Machining accuracy reliability during the peripheral milling process of thin-walled components. Robotics and Computer-Integrated Manufacturing 2019; 59: 222–234, https://doi.org/10.1016/j.rcim....
29.
Zhang ZS. Mechanism research of the contact thermal resistance and its application in thermal analysis of motorized spindles. Beijing: Beijing University of Technology 2018.
30.
Zhao CL, Guan XS. Thermal Analysis and Experimental Study on the Spindle of the High-Speed Machining Center. AASRI Procedia 2012; 1: 207–212, https://doi.org/10.1016/j.aasr....
31.
Zhao HT, Yang JG, Shen JH. Simulation of thermal behavior of a CNC machine tool spindle. International Journal of Machine Tools and Manufacture 2007; 47(6): 1003–1010, https://doi.org/10.1016/j.ijma....
32.
Zhao Y, Lin TM, Tang XQ. Geometric Error Modeling of Machine Tools Based on Screw Theory. Procedia Engineering 2011; 24: 845–849, https://doi.org/10.1016/j.proe....
33.
Zheng DX, Chen WF. Effect of structure and assembly constraints on temperature of high-speed angular contact ball bearings with thermal network method. Mechanical Systems and Signal Processing 2020; 145: 106929, https://doi.org/10.1016/j.ymss....
34.
Zhu SW, Ding GF, Qin SF, Lei J, Zhuang L, Yan KY. Integrated geometric error modeling, identification and compensation of CNC machine tools. International Journal of Machine Tools and Manufacture 2012; 52(1): 24–29, https://doi.org/10.1016/j.ijma....
CITATIONS (1):
1.
Machining accuracy reliability evaluation of CNC machine tools based on the milling stability optimization
Ziling Zhang, Yujie Yang, Guowei Li, Yin Qi, Cong Yue, Yongli Hu, Ying Li
The International Journal of Advanced Manufacturing Technology
Ziling Zhang, Yujie Yang, Guowei Li, Yin Qi, Cong Yue, Yongli Hu, Ying Li
The International Journal of Advanced Manufacturing Technology
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.