Multiobjective Approach for Solving Engineering Robust Design Problems

2021 ◽  
pp. 209-221
Author(s):  
Leoneed Kirilov ◽  
Petar Georgiev
Keyword(s):  
Robust Design ◽  
Design Problems ◽  
Multiobjective Approach

UNCERTAINTY BASED ROBUST OPTIMIZATION IN AERODYNAMICS

2009 ◽  
Vol 23 (03) ◽  
pp. 477-480 ◽  
Author(s):  
ZHILI TANG
Keyword(s):  
Nash Equilibrium ◽  
Robust Design ◽  
Single Point ◽  
Optimization Method ◽  
Equilibrium Solutions ◽  
Design Problems ◽  
Nash Equilibrium Solutions ◽  
Definition Of ◽  
Taguchi Robust Design ◽  
Statistical Definition

The Taguchi robust design concept is combined with the multi-objective deterministic optimization method to overcome single point design problems in Aerodynamics. Starting from a statistical definition of stability, the method finds, Nash equilibrium solutions for performance and its stability simultaneously.

A trade-off function to tackle robust design problems in engineering

2013 ◽  
Vol 24 (1) ◽  
pp. 64-81 ◽  
Author(s):  
Thomas Quirante ◽  
Patrick Sebastian ◽  
Yann Ledoux
Keyword(s):  
Robust Design ◽  
Design Problems ◽  
Trade Off

Understanding the Effects of Model Uncertainty in Robust Design With Computer Experiments

2006 ◽  
Author(s):  
Jun Liu ◽  
Daniel W. Apley ◽  
Wei Chen
Keyword(s):  
Robust Design ◽  
Prediction Interval ◽  
Computer Experiments ◽  
Design Problems ◽  
Deterministic Optimization ◽  
Automotive Engine ◽  
Design Objective ◽  
Design Variables ◽  
Complex Design ◽  
The Impact

The use of metamodels in simulation-based robust design introduces a new source of uncertainty that we term model interpolation uncertainty. Most existing approaches for treating interpolation uncertainty in computer experiments have been developed for deterministic optimization and are not applicable to design under uncertainty. With the randomness present in noise and/or design variables that propagates through the metamodel, the effects of model interpolation uncertainty are not nearly as transparent as in deterministic optimization. In this work, a methodology is developed within a Bayesian framework for quantifying the impact of interpolation uncertainty on robust design objective. By viewing the true response surface as a realization of a random process, as is common in kriging and other Bayesian analyses of computer experiments, we derive a closed-form analytical expression for a Bayesian prediction interval on the robust design objective function. This provides a simple, intuitively appealing tool for distinguishing the best design alternative and conducting more efficient computer experiments. Even though our proposed methodology is illustrated with a simple container design and an automotive engine piston design example here, the developed analytical approach is the most useful when applied to high-dimensional complex design problems in a similar manner.

Alternatives of Taguchi's Approach for Dynamic Robust Design Problems

1998 ◽  
Vol 05 (02) ◽  
pp. 115-131 ◽  
Author(s):  
Kwok-Leung Tsui
Keyword(s):  
Response Function ◽  
Robust Design ◽  
Dynamic Responses ◽  
Response Model ◽  
Function Model ◽  
Design Problems ◽  
Loss Model ◽  
Modeling Approaches ◽  
Noise Factors ◽  
Model Approach

Robust Design is an important method for improving product quality, manufacturability, and reliability at low cost. Most research in robust design has been focused on problems with static responses. This paper deals with the robust design problems with dynamic responses. The objective of the paper is to investigate and compare three modeling approaches: the loss model, the response function model, and the response model approaches. Taguchi16 proposes the loss model approach which models the loss measures as functions of the control factor effects. Miller and Wu10 propose the response function model approach which models the loss measures as functions of the effects of both control and noise factors. Tsui18 proposes the response model approach which directly models the response as a function of the effects of control, noise, and signal factors. In this paper, we identify and derive the relationships between the effect estimates of the three approaches and show that the loss model approach creates unnecessary biases for the factorial effect estimates and may lead to non-optimal solutions. The three modeling approaches are compared in a real example.

A Proposal of Robust Design Method for Deriving Adjustable Range

2011 ◽  
Vol 311-313 ◽  
pp. 2332-2335 ◽  
Author(s):  
Suguru Kimura ◽  
Takeo Kato ◽  
Yoshiyuki Matsuoka
Keyword(s):  
Robust Design ◽  
Design Method ◽  
Previous Method ◽  
Product Performance ◽  
Design Solution ◽  
Control Factor ◽  
Design Problems ◽  
Solution Design ◽  
Adjustable Range ◽  
Robust Design Method

Due to increased individuation of user needs and market globalization, the demands for product performance have diversified. However, a diversified performance is difficult to be obtained a unique design solution (design value). In a previous study, the robust design method (RDM), which ensures products with a robust performance under diverse conditions, was improved. This method was used to evaluate performance robustness with respect to an adjustable control factor (ACF), which is a factor that can be adjusted by the user. Unfortunately, the RDM is not applicable to design problems that have several ACFs due to the increased calculation amount. To resolve this issue, this study improves the previous method by applying a genetic algorithm and a method to extract the values of ACFs employed in the robustness calculation. The improved RDM was applied to two numerical examples to confirm its effectiveness.

scholarly journals Tolerance Synthesis of Mechanisms: A Robust Design Approach

2003 ◽  
Author(s):  
Ste´phane Caro ◽  
Fouad Bennis ◽  
Philippe Wenger
Keyword(s):  
Robust Design ◽  
Optimization Problem ◽  
Synthesis Method ◽  
Upper And Lower Bounds ◽  
Design Parameters ◽  
Design Problems ◽  
Tolerance Synthesis ◽  
Design Variables ◽  
Synthesis Of Mechanisms ◽  
Serial Positioning

The paper aims at dimensioning a mechanism in order to make it robust, and synthesizing its dimensional tolerances. The design of a mechanism is supposed to be robust when its performance is as little as sensitive as possible to variations. First, a distinction is made between three sets to formulate a robust design problem; (i) the set of Design Variables (DV) whose nominal values can be selected between a range of upper and lower bounds, they are controllable; (ii) the set of Design Parameters (DP) that cannot be adjusted by the designer, they are uncontrollable; (iii) the set of performance functions. DV are however under uncontrollable variations although their nominal value can be adjusted. Moreover, two methods are described to solve robust design problems. The first method is explicit and solves problems that aim at minimizing variations in performance. The second method, an optimization problem, aims at optimizing the performance and minimizing its variations, but only when the ranges of variations in DV and DP are known. Besides, we define and compare some robustness indices. From the explicit method, we develop a new tolerance synthesis method. Finally, three examples are included to illustrate these methods: a damper, a two-dof and a three-dof serial positioning manipulator.

Robust Design for Improved Vehicle Handling Under a Range of Maneuver Conditions

1999 ◽  
Author(s):  
Wei Chen ◽  
Raman Garimella ◽  
Nestor Michelena
Keyword(s):  
Robust Design ◽  
Design Procedure ◽  
Robust Design Optimization ◽  
Vehicle Design ◽  
Optimization Approach ◽  
Ground Vehicles ◽  
Dynamic Simulations ◽  
Design Problems ◽  
Vehicle Handling ◽  
Recent Developments

Abstract In this article, a robust design procedure is applied to achieve improved vehicle handling performance as an integral part of simulation-based vehicle design. Recent developments in the field of robust design optimization and the techniques for creating global approximations of design behaviors are applied to improve the computational efficiency of robust vehicle design built upon sophisticated vehicle dynamic simulations. Our approach is applied to the design of a M916A1 6-wheel tractor / M870A2 3-axle semi-trailer. The results illustrate that the proposed procedure is effective for preventing the rollover of ground vehicles as well as for identifying a design that is not only optimal against the worst maneuver condition but is also robust with respect to a range of maneuver inputs. Furthermore, a comparison is made between a statistical approach and a bi-level optimization approach in terms of their effectiveness in solving robust design problems.

Multidisciplinary Robust Design Optimization of an Internal Combustion Engine

2003 ◽  
Vol 125 (1) ◽  
pp. 124-130 ◽  
Author(s):  
Charles D. McAllister ◽  
Timothy W. Simpson
Keyword(s):  
Internal Combustion Engine ◽  
Design Optimization ◽  
Robust Design ◽  
Combustion Engine ◽  
Internal Combustion ◽  
Collaborative Optimization ◽  
Robust Design Optimization ◽  
Optimization Framework ◽  
Design Variables ◽  
Multiobjective Approach

In this paper, we introduce a multidisciplinary robust design optimization formulation to evaluate uncertainty encountered in the design process. The formulation is a combination of the bi-level Collaborative Optimization framework and the multiobjective approach of the compromise Decision Support Problem. To demonstrate the proposed framework, the design of a combustion chamber of an internal combustion engine containing two subsystem analyses is presented. The results indicate that the proposed Collaborative Optimization framework for multidisciplinary robust design optimization effectively attains solutions that are robust to variations in design variables and environmental conditions.

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