DOI: 10.14704/nq.2018.16.6.1643

Analysis of the Radial Stiffness of Rubber Bush Used in Dynamic Vibration Absorber Based on Artificial Neural Network

Lie Li, Beibei Sun, Miao He, Haitao Hua


Rubber bush is used in dynamic vibration absorber as dissipating devices in damping boring bar. These devices actually have to support radial load in compression when chattering occurs. Mastering the behavior of the radial stiffness of the rubber bush implies an accurate understanding of dynamic vibration absorber. The behavior is, however, complex due to the changeable cross-sectional shape and boundary conditions of the rubber bush. By using artificial neural network, the radial stiffness can be predicted efficiently. According to the authors’ knowledge, simulations and tests on radial stiffness of the rubber bush under combined different cross-sectional shape and boundary conditions by using artificial neural network have not been performed yet. The purpose of this study is thus to find the law of radial stiffness of rubber bush under different cross-section shapes and axial pre-compression conditions. In order to achieve this aim, simulations and tests under different chamfering sizes and axial pre-compression by using artificial neural network were first carried out.


Artificial Neural Network, Dynamic Vibration Absorber, Rubber Bush, Radial Stiffness, Axial Pre-Compression

Full Text:



Adkins JE, Gent AN. Load-deflexion relations of rubber bush mountings. British Journal of Applied Physics 1954; 5(10): 354.

Cheng C, Li S, Wang Y. Performance analysis of high-static-low-dynamic stiffness vibration isolator with time-delayed displacement feedback. Journal of Central South University 2017; 24(10): 2294-305.

Cheng JC, Huang P, Xiong C. Spatial prediction of soil nutrition based on BP neural network. Guangdong Agricultural Sciences 2013; 40(7): 1564-86.

Chuong B, Tung NH, Hung DV. Invited review. Natural rubber nanocomposites. Vietnam Journal of Chemistry 2017; 55(6): 663-78.

Cui X, Gao L, Liu JX. Wind tunnel test study on the influence of railing ventilation rate on the vortex vibration characteristics of the main beam. International Journal of Heat and Technology 2018; 36(1): 65-71.

Ehsani M, Shariatmadari N, Mirhosseini SM. Shear modulus and damping ratio of sand-granulated rubber mixtures. Journal of Central South University 2015; 22(8): 3159-67.

Hill JM. Radical deflections of rubber bush mountings of finite lengths. International Journal of Engineering Science 1975; 13(4): 407-22.

Horton JM, Gover MJC, Tupholme GE. Stiffness of rubber bush mountings subjected to radial loading. Rubber chemistry and technology 2000; 73(2): 253-64.

Horton JM, Tupholme GE. Approximate radial stiffness of rubber bush mountings. Materials & Design 2006; 27(3): 226-29.

Kang Y, Huang CC, Lin CS, Shen PC, Chang YP. Stiffness determination of angular-contact ball bearings by using neural network. Tribology International 2006; 39(6): 461-69.

Li L, Sun B. Optimal parameters selection and engineering implementation of dynamic vibration absorber attached to boring bar//INTER-NOISE and NOISE-CON Congress and Conference Proceedings. Institute of Noise Control Engineering 2016; 253(8): 563-70.

Liu X, Liu Q, Wu S. Analysis of the vibration characteristics and adjustment method of boring bar with a variable stiffness vibration absorber. The International Journal of Advanced Manufacturing Technology 2017; 2017: 1-11.

Liu X, Liu Q, Wu S. Research on the performance of damping boring bar with a variable stiffness dynamic vibration absorber. The International Journal of Advanced Manufacturing Technology 2017; 89(9-12): 2893-906.

Markou AA, Manolis GD. Numerical Solutions for Nonlinear High Damping Rubber Bearing Isolators: Newmark’s Method with Netwon-Raphson Iteration Revisited. Journal of Theoretical and Applied Mechanics 2018; 48(1): 46-58.

Maureira N, Llera JDL, Oyarzo C. A nonlinear model for multilayered rubber isolators based on a co-rotational formulation. Engineering Structures 2017; 131: 1-13.

Miguelez MH, Rubio L, Loya JA. Improvement of chatter stability in boring operations with passive vibration absorbers. International Journal of Mechanical Sciences 2010; 52(10): 1376–84.

Qin B, Shao J, Han G. Finite element analyses on radial stiffness of annular rubber in the dynamical vibration absorption boring bar. Machine Design and Research 2008; 24(4): 90-92.

Quintana G, Ciurana J. Chatter in machining processes: A review. International Journal of Machine Tools and Manufacture 2011; 51(5): 363–76.

Rubio L, Loya JA, Miguelez MH. Optimization of passive vibration absorbers to reduce chatter in boring. Mechanical Systems and Signal Processing 2013; 41(1-2): 691-704.

Shuran L, Shujin L. Applying BP neural network model to forecast peak velocity of blasting ground vibration. Procedia Engineering 2011; 26: 257-63.

Siddhpura M, Paurobally R. A review of chatter vibration research in turning. International Journal of Machine Tools and Manufacture 2012; 61: 27–47.

Song W, Cui Z, Yang H. Estimation of Upper Limb Muscle Stiffness Based on Artificial Neural Network. Metrology &Measurement technique 2017; 44(8): 1-3.

Stevenson AC. Some boundary problems of two-dimensional elasticity. Philos. Mag 1943; 34: 766-93.

Yoon JH, Yang IH, Jeong JE. Reliability improvement of a sound quality index for a vehicle HVAC system using a regression and neural network model. Applied Acoustics 2012; 73(11): 1099-103.

Zaoui FZ, Hanifi HA, Abderahman LY, Mustapha MH, Abdelouahed T, Djamel O. Free vibration analysis of functionally graded beams using a higher-order shear deformation theory. Mathematical Modelling of Engineering Problems 2017; 4(1): 7-12.

Supporting Agencies

| NeuroScience + QuantumPhysics> NeuroQuantology :: Copyright 2001-2019