A New Approach to Probability in Engineering Design and Optimization

Siddall, J. N.

doi:10.1115/1.3258562

Cited by 26 publications

(9 citation statements)

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“…This indicates that the reliability-based design methods do not attempt to minimize the variability 53 , and, hence, concentrate on the rare events at the tails of the probability distribution 54 . On the other hand, the RD optimization approach attempts to minimize the variance for a given quality characteristic.…”

Section: Reliability-based Robust Design Optimizationmentioning

confidence: 96%

“…The reliability-based design approach accepts variability and uses the limit state function to separate out the stress and strength probability density functions (pdf) to achieve the desired reliability level. This indicates that the reliability-based design methods do not attempt to minimize the variability 53 , and, hence, concentrate on the rare events at the tails of the probability distribution 54 . On the other hand, the RD optimization approach attempts to minimize the variance for a given quality characteristic.…”

Section: Reliability-based Robust Design Optimizationmentioning

confidence: 99%

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Reliability‐based robust design optimization: A multi‐objective framework using hybrid quality loss function

Yadav

Bhamare

Rathore

2009

Quality & Reliability Eng

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In this globally competitive business environment, design engineers are constantly striving to establish new and effective tools and techniques to ensure a robust and reliable product design. Robust design (RD) and reliability-based design approaches have shown the potential to deal with variability in the life cycle of a product. This paper explores the possibilities of combining both approaches into a single model and proposes a hybrid quality loss function-based multiobjective optimization model. The model is unique because it uses a hybrid form of quality loss-based objective function that is defined in terms of desirable as well as undesirable deviations to obtain efficient design points with minimum quality loss. The proposed approach attempts to optimize the product design by addressing quality loss, variability, and life-cycle issues simultaneously by combining both reliability-based and RD approaches into a single model with various customer aspirations. The application of the approach is demonstrated using a leaf spring design example.

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Section: Reliability-based Robust Design Optimizationmentioning

confidence: 96%

Section: Reliability-based Robust Design Optimizationmentioning

confidence: 99%

Reliability‐based robust design optimization: A multi‐objective framework using hybrid quality loss function

Yadav

Bhamare

Rathore

2009

Quality & Reliability Eng

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“…They are predominantly used for risk analysis by computing the probability of failure of the system. In the reliability approaches, the variation is not minimized (Siddall, 1984) and it is concentrated on the rare events at the tails of the probability distribution. On the contrary, a robust design optimization (RDO) approach is able to fi nd the optimal performance both in terms of mean value and also in terms of minimization of its variability coming from uncertainty (Doltsins and Kang, 2004).…”

Section: Introductionmentioning

confidence: 99%

Fuzzy-entropy based robust optimization criteria for tuned mass dampers

Marano

Quaranta

Sgobba

2010

Earthq. Eng. Eng. Vib.

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Tuned mass dampers (TMD) are well known as one of the most widely adopted devices in vibration control passive strategies. In the past few decades, many methods have been developed to fi nd the optimal parameters of a TMD installed on a structure and subjected to a random base excitation process, but most of them are usually based on an implicit assumption that all of the structural parameters are deterministic. However, in many real cases this simplifi cation is unacceptable, so robust optimal design criteria becomes a viable alternative to better support engineers in the design process. In Robust Design Optimization (RDO) approaches, indeed the solution must be able to not only minimize the performance but also to limit its variation induced by uncertainty. Most of the currently available RDO methods are based on a probabilistic description of the model uncertainty, even if in many cases they are not able to explicitly include the infl uence of all the possible sources of uncertainties. Therefore, in this study, a fuzzy version of the robust TMD design optimization problem is proposed. The consistency of the fuzzy approach is studied with respect to the available non-probabilistic formulations reported in the literature and an application to an example of a robust design of a linear TMD subjected to base random vibrations in the presence of fuzzy uncertainties. The results show that the proposed fuzzy-based approach is able to give a set of optimal solutions both in terms of structural effi ciency and sensitivity to mechanical and environmental uncertainties.

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“…Recently, many authors have introduced procedures that combine design optimization techniques with reliability-based design methods [5,31,53,57,86,93]. The approach allows the designer to minimize the objective function while accounting for the influence of stochastic variations of some parameters and/or variables.…”

Section: Stochastic Multi-objective Optimizationmentioning

confidence: 99%

Optimization and integration of ground vehicle systems

Gobbi

Haque

Papalambros

et al. 2005

Vehicle System Dynamics

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This article deals with the optimal design of ground vehicles and their subsystems, with particular reference to 'active' safety and comfort. A review of state-of-the-art optimization methods for solving vehicle system design problems, including the integration of electronic controls, is given, thus further encouraging the use of such methods as standard tools for automotive engineers. Particular attention is devoted to the class of methods pertaining to complex system design optimization, as well as approaches for the optimal design of complex systems under uncertainty. Some examples of design optimizations are given in the fields of vehicle system dynamics, powertrain/internal combustion engine design, active safety and ride comfort, vehicle system design and lightweight structures, advanced automotive electronics, and smart vehicles.

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A New Approach to Probability in Engineering Design and Optimization

Cited by 26 publications

References 0 publications

Reliability‐based robust design optimization: A multi‐objective framework using hybrid quality loss function

Reliability‐based robust design optimization: A multi‐objective framework using hybrid quality loss function

Fuzzy-entropy based robust optimization criteria for tuned mass dampers

Optimization and integration of ground vehicle systems

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