Dynamic characteristic analysis of whole machine tools based on Kriging model

Abstract

In order to study the variation of machine tools’ dynamic characteristics in the manufacturing space, a Kriging approximate model is proposed. Finite element method (FEM) is employed on the platform of ANSYS to establish finite element (FE) model with the dynamic characteristic of combined interface for a milling machine, which is newly designed for producing aero engine blades by a certain enterprise group in China. The stiffness and damping of combined interfaces are adjusted by using adaptive simulated annealing algorithm with the optimizing software of iSIGHT in the process of FE model update according to experimental modal analysis (EMA) results. The Kriging approximate model is established according to the finite element analysis results utilizing orthogonal design samples by taking into account of the range of configuration parameters. On the basis of the Kriging approximate model, the response surfaces between key response parameter and configuration parameters are obtained. The results indicate that configuration parameters have great effects on dynamic characteristics of machine tools, and the Kriging approximate model is an effective and rapid method for estimating dynamic characteristics of machine tools in the manufacturing space.

Keywords

machine tool / dynamic characteristic / interface / configuration / Kriging model

References

[1] TohC K. Vibration analysis in high speed rough and finish milling hardened steel [J]. Journal of Sound and Vibration, 2004, 278(1/2): 101-115

[2] LeeD G, LeeC S, LeeH G, HwangH Y, KimJ W. Novel applications of composite structures to robots, machine tools and automobiles [J]. Composite Structures, 2004, 66(1/2/3/4): 17-39

[3] LeeS W, MayorR, NiJ. Dynamic analysis of a mesoscale machine tool [J]. Journal of Manufacturing Science and Engineering, 2006, 128(1): 194-203

[4] AbeleE, KorffD. Avoidance of collision-caused spindle damages-challenges, methods and solutions for high dynamic machine tools [J]. CIRP Annals-Manufacturing Technology, 2011, 60(1): 425-428

[5] MaoK-m, LiB, WuJ, ShaoX-yu. Stiffness influential factors-based dynamic modeling and its parameter identification method of fixed joints in machine tools [J]. International Journal of Machine Tools and Manufacture, 2010, 50(2): 156-164

[6] Zaghbani, SongmeneV. Estimation of machine-tool dynamic parameters during machining operation through operational modal analysis [J]. International Journal of Machine Tools and Manufacture, 2009, 49(12/13): 947-957

[7] ZhangG P, HuangY M, ShiW H, FuW P. Predicting dynamic behaviours of a whole machine tool structure based on computer-aided engineering [J]. International Journal of Machine Tools and Manufacture, 2003, 43(7): 699-706

[8] HuoD, ChengK, WardleF. A holistic integrated dynamic design and modelling approach applied to the development of ultraprecision micro-milling machines [J]. International Journal of Machine Tools and Manufacture, 2010, 50(4): 335-343

[9] ZaehM, SiedlD. A new method for simulation of machining performance by integrating finite element and multi-body simulation for machine tools [J]. CIRP Annals-Manufacturing Technology, 2007, 56(1): 383-386

[10] TianH, LiB, LiuH, MaoK, PengF, HuangX. A new method of virtual material hypothesis-based dynamic modeling on fixed joint interface in machine tools [J]. International Journal of Machine Tools and Manufacture, 2011, 51(3): 239-249

[11] GaoX-s, ZhangY-d, ZhangH-w, WuQiong. Effects of machine tool configuration on its dynamics based on orthogonal experiment method [J]. Chinese Journal of Aeronautics, 2012, 25(2): 285-291

[12] LiuH, ZhaoW. Dynamic characteristic analysis for machine tools based on concept of generalized manufacturing space [J]. Chinese Journal of Mechanical Engineering, 2010, 46(21): 54-60

[13] ConnollyR, ThornleyR H. The significance of joint on the overall deflection of machine tool structures [C]. Proceedings of the 6th lnternational Machine Tool Design Research Conference, 1965LondonThe MacMillian Press Ltd139-156

[14] SakataS, AshidaF, TanakaH. Stabilization of parameter estimation for Kriging-based approximation with empirical semivariogram [J]. Computer Methods in Applied Mechanics and Engineering, 2010, 199(25/26/27/28): 1710-1721

[15] KrigeD G. A Statistical approach to some basic mine valuations and allied problems at the Wit-watersrand [J]. Journal of the Chemical, Metallurgical and Miming Society of South Africa, 1951, 52(6): 119-139

[16] ZhangQStructural reliability analysis and optimization based on Kriging technique [D], 2005Dalian, ChinaDalian University of Technology87-100

[17] ZhangH, ZhangY, ZhaoX. Numerical analysis of residual stress field for shot-peening process based on Kriging model [J]. Journal of System Simulation, 2011, 23(4): 826-831