The purpose of this article is to provide a survey of the various stages in the development of response surface methodology RSM. The coverage of these stages is organized in three parts that describe the evolution of RSM since its introduction in the early 1950s. Part I covers the period, 1951-1975, during which the so-called classical RSM was developed. This includes a review of basic experimental designs for fitting linear response surface models, in addition to a description of methods for the determination of optimum operating conditions. Part II, which covers the period, 1976-1999, discusses more recent modeling techniques in RSM, in addition to a coverage of Taguchi's robust parameter design and its response surface alternative approach. Part III provides a coverage of further extensions and research directions in modern RSM. This includes discussions concerning response surface models with random effects, generalized linear models, and graphical techniques for comparing response surface designs. Copyright © 2010 John Wiley & Sons, Inc.

Response Surface Methodology.

Response surface methodology

The original work in response surface methodology (RSM) has been widely used in the chemical and process industries. Recent years have seen more widespread use and new developments in RSM. RMS activities since 1989 are reviewed, and areas of current and..

Response Surface Methodology: A Retrospective and Literature Survey

Desirability function approach: A review and performance evaluation in adverse conditions

Practitioners oftenmust choose optimum operating conditions for several responses simultaneously. Rarely is the resulting "optimum" truly optimal for all of the individual responses taken individually. Instead, the optimum represents ome expli..

A Compromise Approach to Multiresponse Optimization

Desirability functions have been used extensively to simultaneously optimize several responses. Since the original formulation of these functions contains non-differentiable points, only search methods can be used to optimize the overall desirability response. Furthermore, all responses are treated as equally important. We present modified desirability functions that are everywhere differentiable so that more efficient gradient-based optimization methods can be used instead. The proposed functions have the extra flexibility of allowing the analyst to assign different priorities among the responses. The methodology is applied to a wire bonding process that occurs in semiconductor manufacturing, an industrial process where multiple responses are common.

Modified Desirability Functions for Multiple Response Optimization

This bestselling professional reference has helped over 100,000 engineers and scientists with the success of their experiments. The new edition includes more software examples taken from the three most dominant programs in the field: Minitab, JMP, and SAS. Additional material has also been added in several chapters, including new developments in robust design and factorial designs. New examples and exercises are also presented to illustrate the use of designed experiments in service and transactional organizations. Engineers will be able to apply this information to improve the quality and efficiency of working systems.

Student solutions manual : design and analysis of experiments, seventh edition

In many manufacturing settings, a series of gauges is used to screen product before it is shipped to the customer. Semiconductor manufacturing is an example of an industry where this type of serial inspection is used. A common problem is determining the..

Optimal Guard Bands for Gauges in Series

Monitoring, control and improvement of measurement systems performance are an important aspect of process and quality engineering. Gauge and measurement systems capability studies are often used to estimate the inherent variability of error in the gauge. Control charts of measurement error and periodic calibration activities can be employed to keep these systems operating properly. We describe and illustrate the use of a time series model to further improve gauge performance. The model is used to represent non-independent structure in the time series of measurement bias errors. Adjustments to actual process measurements are then based on the estimates of bias error from the model. The technique is applied to a measurement system used in semiconductor manufacturing, resulting in a reduction in the magnitude of measurement error of approximately 40%.

Daniel R. McCarville

Papers

Modified Desirability Functions for Multiple Response Optimization

Student solutions manual : design and analysis of experiments, seventh edition

Optimal Guard Bands for Gauges in Series

A time series approach for compensating for errors in complex gauge systems