William R. Mcdaniel’s research while affiliated with Northwestern University and other places

A method is presented for creating randomly generated polynomial functions to be used as a test bed of simulated response surfaces. The need for the test bed to perform empirical comparisons of experimental design strategies is discussed and the methods used to create the surfaces are explained. An important feature of the test bed is that the user can control some of the characteristics of the surfaces without directly controlling the surface functions. This allows the user to choose the types of surfaces on which a simulation study is run while preserving the random nature of the surfaces needed for a valid simulation study. I. Introduction The experimental study of a response surface for finding optimal or at least desirable settings for the factors is known as Response Surface Methodology (RSM) (see Myers and Montgomery, 1995). Many classes of experimental designs have been developed for RSM, such as factorials, fractional factorials, Box-Behnken designs, and central composite de...

A control chart is proposed for monitoring the capability of a process when sensitivity data is all that is available on a particular measure of interest, such as the threshold height at which a dropped bottle will break. Sensitivity data is data that is collected as either pass or fail of a sample at a certain level exposure to a control variable, such as drop height. Methodology is presented for selecting a certain quantile of the distribution of the measure of interest and monitoring that quantile with a standard p chart. The tradeoffs between increasing the power of the control chart and decreasing the number of measurements are explored. Introduction A special type of data, called sensitivity data, presents a unique set of problems for those wishing to monitor the performance of a process. Sensitivity data refers to data that is collected as a pass or fail of a sample at a certain level or intensity of exposure to a control variable. Dixon and Mood [1] originally presented this ...

The "small factor change" problem, where an experimental design strategy is used to find a certain amount of improvement in a response while changing the factor levels as little as possible, is addressed. Using a recently developed test bed for response surfaces, we have simulated a broad range of response surface functions and collected empirical results on the performance of seven experimental design strategies when confronted with this problem. I. Introduction In this research, a set of experimental design strategies is applied to a situation that we call the small factor change problem to determine which of these strategies performs best on selected measures. The goal of experimentation in the small factor change problem is to gain a specific amount of improvement in a response while changing the factor levels as little as possible. As an example, consider an automobile design problem where there is a specified miles per gallon (MPG) rating desired. Some of the primary factors th...