A Model Updating Method for Plate Elements Using Particle Swarm Optimization (PSO), Modeling the Boundary Flexibility, Including Uncertainties on Material and Dimensional Properties

It is a well-known fact that, in a real engineering situation, fixtures are not ideally stiff, so numerical simulations using them are unlikely to present results that are consistent with the experimental ones. The present paper intends to describe a model updating methodology inserting translational and rotational springs in order to better represent the real clamping. For that purpose, the PSO stochastic optimization method will be used to determine the spring stiffness in an iterative way. In addition, uncertainties regarding the material properties, such as density and Young’s Modulus, as well as workpiece dimensions, will also be taken into account in the optimization algorithm. Once the experimental natural frequencies and the geometry of the studied parts are known, the algorithm automatically updates the model, approximating the natural frequencies obtained from the numerical model to the experimentally obtained ones as closely as possible. In addition, the modal shapes of the updated simulation will be compared to the experimental data and to a rigid boundary simulation. Results will demonstrate that the proposed methodology efficiently represents the fixturing flexibility: both natural frequencies and mode shapes found were close to the real dynamic system.

[1]  Shapour Moradi,et al.  Finite element model updating using bees algorithm , 2010 .

[2]  A. Berman,et al.  Improvement of a Large Analytical Model Using Test Data , 1983 .

[3]  Harmesh Kumar,et al.  Structural Dynamic Model Updating Techniques: A State of the Art Review , 2016 .

[4]  M. Mahendran The Modulus of Elasticity of Steel - Is it 200 GPa? , 1996 .

[5]  Alex Berman,et al.  Optimal weighted orthogonalization of measured modes , 1979 .

[6]  J. Davim,et al.  A study of plastic strain and plastic strain rate in machining of steel AISI 1045 using FEM analysis , 2009 .

[7]  Menahern Baruch,et al.  Optimal Weighted Orttiogonalization of Measured Modes , 1978 .

[8]  Iain M. Boyle,et al.  Review: A review and analysis of current computer-aided fixture design approaches , 2011 .

[9]  S. M. Seyedpoor A two stage method for structural damage detection using a modal strain energy based index and particle swarm optimization , 2012 .

[10]  Kamran Behdinan,et al.  Particle swarm approach for structural design optimization , 2007 .

[11]  Randall J. Allemang,et al.  A Correlation Coefficient for Modal Vector Analysis , 1982 .

[12]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[13]  M J.E. GEOMETRIC PARAMETERS FOR FINITE ELEMENT MODEL UPDATING OF JOINTS AND CONSTRAINTS , .

[14]  Marek Galewski,et al.  Spectrum-based modal parameters identification with Particle Swarm Optimization , 2016 .

[15]  Bijaya Jaishi,et al.  Finite element model updating based on eigenvalue and strain energy residuals using multiobjective optimisation technique , 2007 .

[16]  J. Keith Nisbett,et al.  Shigley's Mechanical Engineering Design , 1983 .

[17]  P. Bussetta,et al.  UPDATING OF A NONLINEAR FINITE ELEMENT MODEL USING DISCRETE-TIME VOLTERRA SERIES , 2017 .

[18]  Wei Wang,et al.  A model updating method for truss structure using stepwise uniform design schemes considered primary factors , 2014 .

[19]  John E. Mottershead,et al.  REGULARISATION METHODS FOR FINITE ELEMENT MODEL UPDATING , 1998 .

[20]  Manolis Papadrakakis,et al.  A Hybrid Particle Swarm—Gradient Algorithm for Global Structural Optimization , 2010, Comput. Aided Civ. Infrastructure Eng..

[21]  Gregory J. Hancock,et al.  TESTS OF PROFILED STEEL DECKS WITH V-STIFFENERS , 1993 .

[22]  Tshilidzi Marwala,et al.  Dynamic Model Updating Using Particle Swarm Optimization Method , 2007, ArXiv.