Optimization of forging processes using Finite Element simulations : A comparison of Sequential Approximate Optimization and other algorithms

Share/Save/Bookmark

Bonte, Martijn H.A. and Fourment, Lionel and Do, Tien-tho and Boogaard van den, A.H. and Huetink, J. (2010) Optimization of forging processes using Finite Element simulations : A comparison of Sequential Approximate Optimization and other algorithms. Structural and Multidisciplinary Optimization, 42 (5). 797 - 810. ISSN 1615-147X

[img]
Preview
PDF
584Kb
Abstract:During the last decades, simulation software based on the Finite Element Method (FEM) has significantly contributed to the design of feasible forming processes. Coupling FEM to mathematical optimization algorithms offers a promising opportunity to design optimal metal forming processes rather than just feasible ones. In this paper Sequential Approximate Optimization (SAO) for optimizing forging processes is discussed. The algorithm incorporates time-consuming nonlinear FEM simulations. Three variants of the SAO algorithm—which differ by their sequential improvement strategies—have been investigated and compared to other optimization algorithms by application to two forging processes. The other algorithms taken into account are two iterative algorithms (BFGS and SCPIP) and a Metamodel Assisted Evolutionary Strategy (MAES). It is essential for sequential approximate optimization algorithms to implement an improvement strategy that uses as much information obtained during previous iterations as possible. If such a sequential improvement strategy is used, SAO provides a very efficient algorithm to optimize forging processes using time-consuming FEM simulations.
Item Type:Article
Copyright:© 2010 The Author(s)
Faculty:
Engineering Technology (CTW)
Research Chair:
Research Group:
Link to this item:http://purl.utwente.nl/publications/73026
Official URL:http://dx.doi.org/10.1007/s00158-010-0545-3
Export this item as:BibTeX
EndNote
HTML Citation
Reference Manager

 

Repository Staff Only: item control page

Metis ID: 267991