International Journal of Material Forming

, Volume 1, Issue 1, pp 13–20

A technique for FEM optimization under reliability constraint of process variables in sheet metal forming

Original Research

DOI: 10.1007/s12289-008-0001-8

Cite this article as:
Strano, M. Int J Mater Form (2008) 1: 13. doi:10.1007/s12289-008-0001-8

Abstract

A method is proposed for the optimization, by finite element analysis, of design variables of sheet metal forming processes. The method is useful when the non-controllable process parameters (e.g. the coefficient of friction or the material properties) can be modelled as random variables, introducing a degree of uncertainty into any process solution. The method is suited for problems with large FEM computational times and small process window. The problem is formulated as the minimization of a cost function, subject to a reliability constraint. The cost function is indirectly optimized through a “metamodel”, built by “Kriging” interpolation. The reliability, i.e. the failure probability, is assessed by a binary logistic regression analysis of the simulation results. The method is applied to the u-channel forming and springback problem presented in Numisheet 1993, modified by handling the blankholder force as a time-dependent variable.

Keywords

KrigingMetamodelBinary logistic regressionReliability

Abbreviation

BHFi

values of the blankholder force vs. time curve

E

operator of expectation

K

hardening coefficient

m

number of components of deterministic vector \(\underline x \)

n

hardening exponent

p

number of components of vector \(\underline \xi \)

pdf

probability density function

PF

probability of failure

q

number of components of vector \(\underline z \)

t%

maximum thinning of the initial sheet thickness t0

w%

percentage increment of the initial channel width w0 after springback

\(\underline w \)

vector of Kriging input variables

\(\underline x \)

vector of deterministic control variables

y

objective function

Y

Young’s modulus

\(\underline z \)

vector of output variables of interest

α

maximum tolerable probability of failure

\(\bar \varepsilon \)

effective strain

\(\underline \varphi \)

mean square error of the Kriging predictor

λ

weights of the objective function

μ

expected value

\(\bar \sigma \)

flow stress

σ

standard deviation

Σ

covariance matrix

Ωs

feasibility window

\(\underline \xi \)

vector of random process variables

Copyright information

© Springer/ESAFORM 2008

Authors and Affiliations

  1. 1.Dipartimento di Ingegneria IndustrialeUniversità di CassinoCassino (FR)Italy