Statistical Process Optimization Through Multi-Response Surface Methodology

Abstract—In recent years, response surface methodology (RSM) has

brought many attentions of many quality engineers in different

industries. Most of the published literature on robust design

methodology is basically concerned with optimization of a single

response or quality characteristic which is often most critical to

consumers. For most products, however, quality is multidimensional,

so it is common to observe multiple responses in an experimental

situation. Through this paper interested person will be familiarize

with this methodology via surveying of the most cited technical

papers.

It is believed that the proposed procedure in this study can resolve

a complex parameter design problem with more than two responses.

It can be applied to those areas where there are large data sets and a

number of responses are to be optimized simultaneously. In addition,

the proposed procedure is relatively simple and can be implemented

easily by using ready-made standard statistical packages.

Keywords—Multi-Response Surface Methodology (MRSM),

Design of Experiments (DOE), Process modeling, Quality

improvement; Robust Design.

I. INTRODUCTION

ESPONSE Surface Methodology (RSM) is a well known

up to date approach for constructing approximation

models based on either physical experiments, computer

experiments (simulations) (Box et al., [1] ; Montgomery, [2])

and experimented observations. RSM, invented by Box and

Wilson, is a collection of mathematical and statistical

techniques for empirical model building. By careful design of

experiments, the objective is to optimize a response (output

variable) which is influenced by several independent variables

(input variables). An experiment is a series of tests, called

runs, in which changes are prepared in the input variables in

order to recognize the reasons for changes in the output

response (Montgomery & Runger [3]). RSM involves two

basic concepts:

(1) The choice of the approximate model, and

(2) The plan of experiments where the response has to be

evaluated.

The performance of a manufactured product often

characterize by a group of responses. These responses in

general are correlated and measured via a different

measurement scale. Consequently, a decision-maker must

resolve the parameter selection problem to optimize each

response. This problem is regarded as a multi-response

optimization problem, subject to different response

requirements. Most of the common methods are incomplete in

such a way that a response variable is selected as the primary

R. Eslami Farsani is with Islamic Azad University, Tehran South Branch.

one and is optimized by adhering to the other constraints set

by the criteria. Many heuristic methodologies have been

developed to resolve the multi-response problem. Cornell and

Khuri [4] surveyed the multi-response problem using a

response surface method. Tai et al. [5] assigned a weight for

each response to resolve the problem. Pignatiello [6] utilized a

squared deviation-from-target and a variance to form an

expected loss function for optimizing a multiple response

problem. Layne [7] presented a procedure capable of

simultaneously considering three functions: weighted loss

function, desirability function, and distance function. While

providing a multi-response example in which Taguchi

methods are used, Byrne and Taguchi [8] discussed an

example involving a connector and a tube.

Logothetis and Haigh [9] also discussed a manufacturing

process differentiated by five responses. In doing so, they

selected one of the five response variables as primary and

optimized the objective function sequentially while ignoring

possible correlations among the responses. Optimizing the

process with respect to any single response leads to nonoptimum

values for the remaining characteristics.

II. RESPONSE SURFACE METHODOLOGY

Often engineering experimenters wish to find the conditions

under which a certain process attains the optimal results. That

is, they want to determine the levels of the design parameters

at which the response reaches its optimum. The optimum

could be either a maximum or a minimum of a function of the

design parameters. One of methodologies for obtaining the

optimum

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