Enhancing CAD-based shape optimization by automatically updating the CAD model’s parameterization

Dheeraj Agarwal, Trevor T. Robinson, Cecil G. Armstrong, Christos Kapellos

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
186 Downloads (Pure)


This paper presents an approach which increases the flexibility of a computer-aided design (CAD) model by automatically refining its parameterization and adding new CAD features to the model´s feature tree. It aims to overcome the limitations imposed by the choice of parameters used during the initial model creation, which constrains how the model shape can change during design optimization. Parametric Effectiveness compares the maximum performance improvement that can be achieved using a parameterization strategy, to the maximum performance improvement that can be obtained where the model is unconstrained in how it moves. As such, it provides a measure of how good a parameterization strategy is and allows different strategies to be compared. The change in parametric effectiveness due to inserting multiple different CAD features can be calculated using a single adjoint analysis, therefore the computational cost is essentially independent of the number of parameterisation strategies being analysed. The described approach can be used to automatically add new features to the model, and subsequently allows the use of the newly-added parameters, along with the existing parameters to be used for optimization, providing the opportunity for a better performing product. The developed approach is applied on CAD models created in CATIA V5 for 2D and 3D finite element and computational fluid dynamics problems.
Original languageEnglish
Pages (from-to)1639
Number of pages16
JournalStructural and Multidisciplinary Optimization
Issue number2019
Early online date28 Nov 2018
Publication statusEarly online date - 28 Nov 2018

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