Optimal design of interference fit assemblies subjected to fatigue loads

This paper presents a methodology for an optimal design of interference fit subjected to fatigue loads. Optimization consists in finding a trade-off between mass and competing safety factors at hub and shaft contact zone as well as in shaft fillet. Developing an effective calculation method for fatigue strength of an interference fitted assembly using the finite element method is one of the main steps of the procedure. Meanwhile, coupling the finite elements model of interference fit with an optimization algorithm is not adequate considering the computing time and the significant number of calculations necessary to portrait the assembly behavior. Therefore, a sequential approximate multi-objective optimization algorithm (SAMOO) is presented. The method involves Design Of Experiments (DOE), interpolation with kriging functions, and multi-objective optimization. Preliminary study of parameter variance, and advanced post-processing of multi-objective optimization, provide engineers with valuable information for identifying an optimal design of interference fit assembly using fewer finite element calculations.

[1]  Shapour Azarm,et al.  A Kriging Metamodel Assisted Multi-Objective Genetic Algorithm for Design Optimization , 2008 .

[2]  Teuvo Juuma,et al.  Torsional fretting fatigue strength of a shrink-fitted shaft with a grooved hub , 2000 .

[3]  F. van Keulen,et al.  Framework for sequential approximate optimization , 2004 .

[4]  Jack P. C. Kleijnen,et al.  An Overview of the Design and Analysis of Simulation Experiments for Sensitivity Analysis , 2005, Eur. J. Oper. Res..

[5]  Bernard Sanschagrin,et al.  Fretting fatigue strength reduction factor for interference fits , 2011, Simul. Model. Pract. Theory.

[6]  Christina Schäfer,et al.  Shape optimisation by design of experiments and finite element methods—an application of steel wheels , 2008 .

[7]  M. E. Johnson,et al.  Minimax and maximin distance designs , 1990 .

[8]  Søren Nymand Lophaven,et al.  DACE - A Matlab Kriging Toolbox , 2002 .

[9]  M. D. McKay,et al.  A comparison of three methods for selecting values of input variables in the analysis of output from a computer code , 2000 .

[10]  Teuvo Juuma,et al.  Torsional fretting fatigue strength of a shrink-fitted shaft , 1999 .

[11]  S. Azarm,et al.  Improving multi-objective genetic algorithms with adaptive design of experiments and online metamodeling , 2009 .

[12]  Sean B. Leen,et al.  Contact-evolution based prediction of fretting fatigue life: Effect of slip amplitude , 2007 .

[13]  Y. Kondo,et al.  Effect of stress relief groove on fretting fatigue strength and index for the selection of optimal groove shape , 2009 .

[14]  Kunio Nishioka,et al.  Researches on Increasing the Fatigue Strength of Press-Fitted Shaft Assembly , 1967 .

[15]  Bernard Sanschagrin,et al.  Finite element analysis and contact modelling considerations of interference fits for fretting fatigue strength calculations , 2009, Simul. Model. Pract. Theory.

[16]  Jack P. C. Kleijnen,et al.  Customized sequential designs for random simulation experiments: Kriging metamodeling and bootstrapping , 2008, Eur. J. Oper. Res..

[17]  T. J. Mitchell,et al.  Exploratory designs for computational experiments , 1995 .

[18]  Lionel Fourment,et al.  Optimization of forging processes using Finite Element simulations , 2010 .

[19]  Thomas J. Santner,et al.  The Design and Analysis of Computer Experiments , 2003, Springer Series in Statistics.

[20]  Jongsoo Lee,et al.  An improvement of Kriging based sequential approximate optimization method via extended use of design of experiments , 2010 .

[21]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[22]  L. Nilsson,et al.  On polynomial response surfaces and Kriging for use in structural optimization of crashworthiness , 2005 .