Quality Engineering

A vulnerability assessment of a combat vehicle uses modeling and simulation (M&S) to predict the vehicle’s vulnerability to a given enemy attack. The system-level output of the M&S is the probability that the vehicle’s mobility is degraded as a result of the attack. The M&S models this system-level phenomenon by decoupling the attack scenario into a hierarchy of sub-systems. Each sub-system addresses a specific scientific problem, such as the fracture dynamics of an exploded munition, or the ballistic resistance provided by the vehicle’s armor....

Sequential analysis concerns statistical evaluation in which the number, pattern, or composition of the data is not determined at the start of the investigation, but instead depends on the information acquired during the investigation. Although sequential analysis originated in ballistics testing for the Department of Defense (DoD)and it is widely used in other disciplines, it is underutilized in the DoD. Expanding the use of sequential analysis may save money and reduce test time....

Engineers use reliability experiments to determine the factors that drive product reliability, build robust products, and predict reliability under use conditions. This article uses recent testing of a Howitzer to illustrate the challenges in designing reliability experiments for complex, repairable systems. We leverage lessons learned from current research and propose methods for designing an experiment for a complex, repairable system. Suggested Citation Freeman, Laura J., Rebecca M. Medlin, and Thomas H....

Statistical literature refers to a type of error that is committed by giving the right answer to the wrong question. If a test design is adequately scoped to address an irrelevant objective, one could say that a Type III error occurs. In this paper, we focus on a specific Type III error that on some occasions test planners commit to reduce test size and resources. Suggested Citation Johnson, Thomas H., Rebecca M....

Statistical power is a useful measure for assessing the adequacy of anexperimental design prior to data collection. This paper proposes an approach referredto as the signal-to-noise transformation method (SNRx), to approximate power foreffects in a generalized linear model. The contribution of SNRx is that, with a coupleassumptions, it generates power approximations for generalized linear model effectsusing F-tests that are typically used in ANOVA for classical linear models.Additionally, SNRx follows Ohlert and Whitcomb’s unified approach for sizing aneffect, which allows for intuitive effect size definitions, and consistent estimates ofpower....

One of the most powerful features of Bayesian analyses is the ability to combine multiple sources of information in a principled way to perform inference. This feature can be particularly valuable in assessing the reliability of systems where testing is limited. At their most basic, Bayesian methods for reliability develop informative prior distributions using expert judgment or similar systems. Appropriate models allow the incorporation of many other sources of information, including historical data, information from similar systems, and computer models....

This tutorial outlines the basic procedures for planning experiments within the context of the scientific method. Too often quality practitioners fail to appreciate how subject-matter expertise must interact with statistical expertise to generate efficient and effective experimental programs. This tutorial guides the quality practitioner through the basic steps, demonstrated by extensive past experience, that consistently lead to successful results. This tutorial makes extensive use of flowcharts to illustrate the basic process....

Space-Filling Designs are a common choice of experimental design strategy for computer experiments. This paper compares space filling design types based on their theoretical prediction variance properties with respect to the Gaussian Process model. Suggested Citation Silvestrini, Rachel T., Douglas C. Montgomery, and Bradley Jones. “Comparing Computer Experiments for the Gaussian Process Model Using Integrated Prediction Variance.” Quality Engineering 25, no. 2 (April 2013): 164–74. https://doi.org/10.1080/08982112.2012.758284. Paper:

The areas of application for design of experiments principles have evolved, mimicking the growth of U.S. industries over the last century, from agriculture to manufacturing to chemical and process industries to the services and government sectors. In addition, statistically based quality programs adopted by businesses morphed from total quality management to Six Sigma and, most recently, statistical engineering (see Hoerl and Snee 2010). The good news about these transformations is that each evolution contains more technical substance, embedding the methodologies as core competencies, and is less of a ‘‘program....

There are many situations where the requirements of a standard experimental design do not fit the research requirements of the problem. Three such situations occur when the problem requires unusual resource restrictions, when there are constraints on the design region, and when a non-standard model is expected to be required to adequately explain the response. Suggested Citation Johnson, Rachel T., Douglas C. Montgomery, and Bradley A. Jones. “An Expository Paper on Optimal Design....