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Modelling and process optimization for functionally graded materials
Author(s) -
BellurRamaswamy Ravi S.,
Haber Robert,
Sobh Nahil A.,
Tortorelli Daniel A.
Publication year - 2004
Publication title -
international journal for numerical methods in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.421
H-Index - 168
eISSN - 1097-0207
pISSN - 0029-5981
DOI - 10.1002/nme.1166
Subject(s) - finite element method , sensitivity (control systems) , galerkin method , boundary value problem , mathematical optimization , mathematics , mathematical analysis , thermodynamics , physics , engineering , electronic engineering
We optimize continuous quench process parameters to produce functionally graded aluminium alloy extrudates. To perform this task, an optimization problem is defined and solved using a standard non‐linear programming algorithm. Ingredients of this algorithm include (1) the process parameters to be optimized, (2) a cost function: the weighted average of the precipitate number density distribution, (3) constraint functions to limit the temperature gradient (and hence distortion and residual stress) and exit temperature, and (4) their sensitivities with respect to the process parameters. The cost and constraint functions are dependent on the temperature and precipitate size which are obtained by balancing energy to determine the temperature distribution and by using a reaction‐rate theory to determine the precipitate particle sizes and their distributions. Both the temperature and the precipitate models are solved via the discontinuous Galerkin finite element method. The energy balance incorporates non‐linear boundary conditions and material properties. The temperature field is then used in the reaction rate model which has as many as 10 5 degrees‐of‐freedom per finite element node. After computing the temperature and precipitate size distributions we must compute their sensitivities. This seemingly intractable computational task is resolved thanks to the discontinuous Galerkin finite element formulation and the direct differentiation sensitivity method. A three‐dimension example is provided to demonstrate the algorithm. Copyright © 2004 John Wiley & Sons, Ltd.