A Multi-Objective Reliability-based Robust Design Optimization Framework Using Hybrid Quality Loss Function

2008 ◽  
Author(s):  
Sunil Bhamare ◽  
Ajay Pal Singh Rathore ◽  
Om Prakash Yadav
Keyword(s):  
Design Optimization ◽  
Robust Design ◽  
Loss Function ◽  
Robust Design Optimization ◽  
Quality Loss ◽  
Quality Loss Function ◽  
Multi Objective ◽  
Optimization Framework

Alternative Methods for Reliability-Based Robust Design Optimization Including Dimension Reduction Method

2006 ◽  
Author(s):  
Ikjin Lee ◽  
Kyung K. Choi ◽  
Liu Du
Keyword(s):  
Design Optimization ◽  
Robust Design ◽  
Reduction Method ◽  
Statistical Moments ◽  
Robust Design Optimization ◽  
Quality Loss ◽  
Dimension Reduction Method ◽  
Quality Loss Function ◽  
Performance Function ◽  
Design Variables

The objective of reliability-based robust design optimization (RBRDO) is to minimize the product quality loss function subject to probabilistic constraints. Since the quality loss function is usually expressed in terms of the first two statistical moments, mean and variance, many methods have been proposed to accurately and efficiently estimate the moments. Among the methods, the univariate dimension reduction method (DRM), performance moment integration (PMI), and percentile difference method (PDM) are recently proposed methods. In this paper, estimation of statistical moments and their sensitivities are carried out using DRM and compared with results obtained using PMI and PDM. In addition, PMI and DRM are also compared in terms of how accurately and efficiently they estimate the statistical moments and their sensitivities of a performance function. In this comparison, PDM is excluded since PDM could not even accurately estimate the statistical moments of the performance function. Also, robust design optimization using DRM is developed and then compared with the results of RBRDO using PMI and PDM. Several numerical examples are used for the two comparisons. The comparisons show that DRM is efficient when the number of design variables is small and PMI is efficient when the number of design variables is relatively large. For the inverse reliability analysis of reliability-based design, the enriched performance measure approach (PMA+) is used.

A Hybrid Quality Loss Function–Based Multi-Objective Design Optimization Approach

2009 ◽  
Vol 21 (3) ◽  
pp. 277-289 ◽  
Author(s):  
Sunil S. Bhamare ◽  
Om Prakash Yadav ◽  
Ajay Rathore
Keyword(s):  
Design Optimization ◽  
Loss Function ◽  
Optimization Approach ◽  
Quality Loss ◽  
Quality Loss Function ◽  
Multi Objective

ERGO: a new Robust Design Optimization Technique combining Multi-Objective Bayesian Optimization with Analytical Uncertainty Quantification

2021 ◽  
pp. 1-22
Author(s):  
Jolan Wauters
Keyword(s):  
Design Optimization ◽  
Uncertainty Quantification ◽  
Robust Design ◽  
Bayesian Optimization ◽  
Robust Design Optimization ◽  
The Novel ◽  
Analytical Uncertainty ◽  
Multi Objective ◽  
Optimization Framework ◽  
Quantities Of Interest

Abstract In this work, robust design optimization (RDO) is treated, motivated by the increasing desire to account for variability the design phase. The problem is formulated in a multi-objective setting with the objective of simultaneously minimizing the mean of the objective and its variance due to variability of design variables and/or parameters. This allows the designer to choose its robustness level without the need to repeat the optimization as typically encountered when formulated as a single objective. To account for the computational cost that is often encountered in RDO problems, the problem is fitted in a Bayesian optimization framework. The use of surrogate modeling techniques to efficiently solve problems under uncertainty has effectively found its way in the optimization community leading to surrogate-assisted optimization-under-uncertainty schemes. The surrogates are often considered cheap-to-sample black-boxes and are sampled to obtain the desired quantities of interest. However, since the analytical formulation of the surrogates is known, an analytical treatment of the problem is available. To obtain the quantities of interest without sampling an analytical uncertainty propagation through the surrogate is presented. The multi-objective Bayesian optimization framework and the analytical uncertainty quantification are linked together through the formulation of the robust expected improvement (REI), obtaining the novel efficient robust global optimization (ERGO) scheme. The method is tested on a series of test cases to examine its behavior for varying difficulties and validated on an aerodynamic test function which proves the effectiveness of the novel scheme.

Robustness Metrics for Time-Dependent Quality Characteristics

2011 ◽  
Author(s):  
Xiaoping Du
Keyword(s):  
Design Optimization ◽  
Robust Design ◽  
Quality Characteristics ◽  
Time Dependent ◽  
Robust Design Optimization ◽  
Time Interval ◽  
Mechanism Analysis ◽  
Quality Loss ◽  
Time Period ◽  
Robustness Metrics

Quality characteristics (QC’s) are often treated static in robust design optimization while many of them are time dependent in reality. It is therefore desirable to define new robustness metrics for time-dependent QC’s. This work shows that using the robustness metrics of static QC’s for those of time-dependent QC’s may lead to erroneous design results. To this end, we propose the criteria of establishing new robustness metrics for time-dependent QC’s and then define new robustness metrics. Instead of using a point expected quality loss over the time period of interest, we use the expectation of the maximal quality loss over the time period to quantify the robustness for time-dependent QC’s. Through a four-bar function generator mechanism analysis, we demonstrate that the new robustness metrics can capture the full information of robustness of a time-dependent QC over a time interval. The new robustness metrics can then be used as objective functions for time-dependent robust design optimization.

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