Adaptive Refined-Model-Based Approach for Robust Design Optimization

Adaptive Refined-Model-Based Approach for Robust Design Optimization

Tanmoy Chatterjee (IIT Roorkee, India) and Rajib Chowdhury (IIT Roorkee, India)
DOI: 10.4018/978-1-5225-4766-2.ch002


Robust design optimization (RDO) has been noteworthy in realizing optimal design of engineering systems in presence of uncertainties. However, computations involved in RDO prove to be intensive for real-time applications. For addressing such issues, a meta-model-assisted RDO framework has been proposed. It has been further observed in such approximation-based RDO frameworks that accuracy of the meta-model is an important factor and even slight deviation in intermediate iterations may eventually lead to false optima. Therefore, two-tier improvement has been incorporated within existing Kriging model so as to ensure accurate approximation of response quantities. Firstly, the trend portion has been refined so that the model is capable of approximating higher order non-linearity. Secondly, a sequential basis selection scheme has been merged during model building, which reduces computational complexity significantly in case of large-scale systems. Implementation of the proposed approach in a few examples clearly illustrates its potential for further complex problems.
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1. Introduction

1.1 General

In order to design and manufacture high quality products at minimum costs, techniques are essential which are able to find designs which meet the requirements usually specified by objectives at the advent of a design process (Simpson, Mauery, Korte, Mistree, 1998; Spall 2003). The process of search and finding the suitable design parameters is referred to as optimization. Additionally, the design constraints imposed on the design parameters have to be satisfied in the process. Such constraints can be modeled by inequalities and/or equalities restricting the design space.

The inevitable role of optimization is well established in engineering science and technology. Advanced modelling and optimization algorithms constitute an essential portion in the design and operation of structural/mechanical systems. Edge cutting performances of manufactured industrial products have been the consequences of employing advanced level design optimization tools. In the design of large and complex systems, the use of efficient optimization tools lead to better product quality and improved functionality. In dealing with large and complex systems, efficient algorithms of engineering design optimization have been prerequisites at every stage for reducing cost, time and increase functionality in substance (Deb 2001; Marler & Arora 2004; Arora, 2012).

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