Multiresponse robust design: Mean square error (MSE) criterion

Köksoy, Onur

doi:10.1016/j.amc.2005.09.016

Cited by 89 publications

(45 citation statements)

References 13 publications

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“…이 중 손실함 수법은 널리 활용되는 다중반응표면 최적화 기법 중 하 나로, 다구찌(Taguchi)의 이차 손실함수(quadratic loss function)에서 파생된 기법이다 [10][11][12]. 이후부터 언급되 는 손실함수는 이차 손실함수를 의미한다.…”

Section: 서론unclassified

A Weighted Mean Squared Error Approach to Multiple Response Surface Optimization

Jeong¹,

Cho²

2013

Journal of the Korea Academia-Industrial cooperation Society

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Multiple response surface optimization (MRSO) aims at finding a setting of input variables which simultaneously optimizes multiple responses. The minimization of mean squared error (MSE), which consists of the squared bias and variance terms, is an effective way to consider the location and dispersion effects of the responses in MRSO. This approach basically assumes that both the terms have an equal weight. However, they need to be weighted differently depending on a problem situation, for example, in case that they are not of the same importance. This paper proposes to use the weighted MSE (WMSE) criterion instead of the MSE criterion in MRSO to consider an unequal weight situation.

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Section: 서론unclassified

A Weighted Mean Squared Error Approach to Multiple Response Surface Optimization

Jeong¹,

Cho²

2013

Journal of the Korea Academia-Industrial cooperation Society

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“…The weighted p ‐norm method applies the p ‐norm to the functional components to obtain optimal solutions. Specific versions of this method have appeared in the RSM literature . This approach generates a variety of multiobjective methods such as displaced ideal method, GP, global criterion, neutral compromise solution, and weighting method.…”

Section: Review Of Multiobjective Approachesmentioning

confidence: 99%

An Overview of Optimization Formulations for Multiresponse Surface Problems

Ardakani

Wulff

2012

Quality & Reliability Eng

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Many industrial applications involve more than one quality characteristic. For example, in robust parameter design, the quality characteristics include the process mean and process variance. Such applications lead to multiresponse surface problems in which it is necessary to determine optimal operating conditions according to some specified optimization criterion involving the quality characteristics. The purpose of this article is to address this problem from a multiobjective decision‐making framework. The foremost approaches in multiresponse optimization are categorized and integrated. Guidelines are presented to help select appropriate formulations. Moreover, the applicability and computational aspects of the methods in various decision‐making contexts are discussed. Numerical examples are also provided. Copyright © 2012 John Wiley & Sons, Ltd.

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“…Equally important, MSE is a multi-objective optimization technique that, according to Köksoy [34], is characterized by the sum of the variance and the square of the difference between the target value and the mean of the response. Minimizing the MSE allows the mean value of the response to approximate the target value and successively the lowest value for the variability.…”

Section: Mean Square Errormentioning

confidence: 99%

A Weighted Mean Square Error Approach to the Robust Optimization of the Surface Roughness in an AISI 12L14 Free-Machining Steel-Turning Process

2018

SV-JME

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Manufacturing processes are aimed at transforming materials into goods, generating wealth [1]. Camposeco-Negrete [2] states that cost and quality are the main goals of manufacturing companies. To improve quality in this type of process, several authors have studied the turning process using mathematical strategies in order to contribute to the efficiency of these processes, such as: the Taguchi method The primary input parameters in the turning process, i.e., cutting speed, feed rate and depth of cut [7], are directly responsible for the quality and productivity characteristics of the process, such as the amount of material removed, tool wear, and finishing of the product [8].Furthermore, the finishing of the machined parts can be evaluated according to the surface roughness, which are irregularities presented on the surface of the parts, characterized by grooves made by the tool during the machining process [9] of the cutting parameters (ranging from a single parameter per experiment) in the quality responses, such as tool life and surface roughness. This paper makes use of only one roughness parameter (arithmetic average roughness, R a ), considering the calculation of its metrics of the roughness characteristics (R a , R y , R q , R z and R t ). The average arithmetic roughness (R a ) is the arithmetic mean of the absolute values of the ordinates of the effective (measured) profile in relation to the midline in a sample length (Fig. 1). In addition, R a is the most used parameter for general quality control [10].The steel used in the turning process of this study was AISI 12L14 carbon steel (used in studies such as: Peruchi et al.

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Multiresponse robust design: Mean square error (MSE) criterion

Cited by 89 publications

References 13 publications

A Weighted Mean Squared Error Approach to Multiple Response Surface Optimization

A Weighted Mean Squared Error Approach to Multiple Response Surface Optimization

An Overview of Optimization Formulations for Multiresponse Surface Problems

A Weighted Mean Square Error Approach to the Robust Optimization of the Surface Roughness in an AISI 12L14 Free-Machining Steel-Turning Process

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