Optimization of multi-response dynamic systems using multiple regression-based weighted signal-to-noise ratio

Gauri, Susanta Kumar; Pal, Surajit

doi:10.5267/j.ijiec.2016.6.001

Cited by 3 publications

(2 citation statements)

References 38 publications

(82 reference statements)

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“…The weighted MRDSN is considered as the objective function for the optimization problem. Gauri and Pal (2017) argue that a multiple regressionbased weighted S/N ratio (MRWSN) performance metric can overcome the limitations of the composite desirability function (CDF) for the multi-objective problems. The results have shown that the MRWSN method is superior to the CDF method in terms of optimization performance.…”

Section: Literature Reviewmentioning

confidence: 99%

Use of the stochastic frontier and the grey relational analysis in robust design of multi-objective problems

Rezgui

Trabelsi

2020

Concurrent Engineering

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A generic procedure for robust design in developing products and processes, which is referred to as RDPP-SF has been proposed. The method uses the stochastic frontier model to encompass both stochastic noise (e.g. manufacturing unit-to-unit variation, and measurement errors) and special-cause variation (e.g. environment, customer use, wearing, and deterioration noises). Even then, the RDPP-SF method has fallen short of tackling robust design of multi-objective problems, and its applicability is restrained to the performance characteristics of magnitude type (i.e., “the larger is the better” or “the smaller is the better”). Aiming at these limitations, the article seeks to address the robust design of the multi-objective problems using the RDPP-SF method. This is performed by reassessing the procedural scheme of the RDPP-SF method and the statistical significance of the hypothesis test (H0: γ = 0 vs H1: γ > 0) at 5% level. Depending on the statistical significance of the test (H0: γ = 0 vs H1: γ > 0), the arrays of the extrinsic and/or the intrinsic noise insensitivity scores are assigned to the grey relational analysis matrix as performance measures. The most robust design solution for the multi-objective problem is then obtained by sorting the overall grey relational grades. The amended RDPP-SF method is finally demonstrated using three industrial multi-objective case studies.

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Section: Literature Reviewmentioning

confidence: 99%

Use of the stochastic frontier and the grey relational analysis in robust design of multi-objective problems

Rezgui

Trabelsi

2020

Concurrent Engineering

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“…Al-Refaie et al [13] integrated the desirability function and data envelopment analysis for solving dynamic systems with multi-responses. Gauri and Pal [14] proposed a multiple regression-based weighted signal-to-noise ratio (MRWSN) to optimize the multi-response dynamic systems.…”

Section: Introductionmentioning

confidence: 99%

Optimal Threshold Value of Digital Dynamic System

Wu¹,

Chuang²,

Lee³

et al. 2019

IJCEE

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Taguchi digital dynamic system are problems in which both the input signal and the output responses have values of either 0 or 1 with two misclassification probabilities. Taguchi proposed a two-step procedure for the digital dynamic system under an equalized error rates model and maximized the standardized SN ratio to achieve robust design. This paper proposes a quality loss model to determine the optimal threshold value of a digital dynamic system based on normally distributed data with unequal loss coefficients. We varied the variances of random variable X1 with increment of 0.10 using Excel software. The results are compared with the threshold values obtained by using Taguchi method. When the two loss coefficients and variances of two populations are equal, the optimal threshold value is the same as the threshold value provided by Taguchi method. The maximum error of optimal threshold value is 0.03 compared with actual threshold value.

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Optimization of multi-response dynamic systems using multiple regression-based weighted signal-to-noise ratio

Gauri¹,

Pal²

2017

10.5267/j.ijiec

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A dynamic system differs from a static system in that it contains signal factor and the target value depends on the level of the signal factor set by the system operator. The aim of optimizing a multi-response dynamic system is to find a setting combination of input controllable factors that would result in optimum values of all response variables at all signal levels. The most commonly used performance metric for optimizing a multi-response dynamic system is the composite desirability function (CDF). The advantage of using CDF is that it is a simple unit less measure and it has a good foundation in statistical practice. However, the problem with the CDF is that it does not consider the variability of the individual response variables. Moreover, if the specification limits for the response variables are not provided the CDF cannot be computed. In this paper, a new performance metric for multi-response dynamic system, called multiple regression-based weighted signal-to-noise ratio (MRWSN) is proposed, which overcome the limitations of CDF. Two sets of experimental data on multi-response dynamic systems, taken from literature, are analysed using both CDF-based and the proposed MRWSN-based approaches for optimization. The results show that the MRWSN-based approach also results in substantially better optimization performance than the CDF-based approach.

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Optimization of multi-response dynamic systems using multiple regression-based weighted signal-to-noise ratio

Cited by 3 publications

References 38 publications

Use of the stochastic frontier and the grey relational analysis in robust design of multi-objective problems

Use of the stochastic frontier and the grey relational analysis in robust design of multi-objective problems

Optimal Threshold Value of Digital Dynamic System

Optimization of multi-response dynamic systems using multiple regression-based weighted signal-to-noise ratio

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