Case Studies

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  1. Employing Power to "Right-Size" Design of Experiments

    March 2014

    This article provides insights on how many runs are required to make it very likely that a test will reveal any important effects. Due to the mathematical complexities of multifactor Design of Experiments (DOE) matrices, the calculations for adequate power and precision (Oehlert and Whitcomb 2002) are not practical to do by 'hand' so the focus is kept at a high level--scoping out the forest rather than detailing all the trees. By example, reader will learn the price that must be paid for an adequately-sized experiment and the penalty incurred by conveniently grouping hard-to-change factors. (The article is not available on the ITEA Journal web site without membership. Click on the Download PDF link below to view the manuscript.)

    Authors: Anderson, Mark J.; Whitcomb, Patrick J.
    Publication: The ITEA Journal
  2. Practical Aspects for Designing Statistically Optimal Experiments

    March 2014
    Due to operational or physical considerations, standard factorial and response surface method (RSM) design of experiments (DOE) often prove to be unsuitable. In such cases a computer-generated statistically-optimal design fills the breech. This article explores vital mathematical properties for evaluating alternative designs with a focus on what is really important for industrial experimenters. To assess “goodness of design” such evaluations must consider the model choice, specific optimality criteria (in particular D and I), precision of estimation based on the fraction of design space (FDS), the number of runs to achieve required precision, lack-of-fit testing, and so forth. With a focus on RSM, all these issues are considered at a practical level, keeping engineers and scientists in mind. This brings to the forefront such considerations as subject-matter knowledge from first principles and experience, factor choice and the feasibility of the experiment design.
    Authors: Anderson, Mark J.; Whitcomb, Patrick J.
    Publication: Journal of Statistical Science and Application V2, N3, March, pp 85-92
  3. Making Use of Mixture Design to Optimize Olive Oil - A Case Study

    August 2009

    Olive oil, an important commodity of the Mediterranean region and a main ingredient of their world-renowned diet (see sidebar), must meet stringent European guidelines to achieve the coveted status of "extra virgin." Oils made from single cultivars (a particular cultivated variety of the olive tree) will at times fall into the lower "virgin" category due to seasonal variation. Then it becomes advantageous to blend in one or more superior oils based on a mixture design for optimal formulation.

    Authors: Anderson, Mark J., Whitcomb, Patrick J.
    Publication: Chemical and Process Industries
  4. Response Surface Methods (RSM) for Peak Process Performance at the Most Robust Operating Conditions

    October 2007

    This article starts with the basics on RSM before introducing two enhancements that focus on robust operating conditions: Modeling the process variance as a function of the input factors and Propagation of error(POE) transmitted from input factor variation. It discusses how to find the find the flats high plateaus for maximum yield and broad valleys that minimize defects. Proceeding from International SEMATECH Manufacturing Initiative (ISMI) Symposium on Manufacturing Effectiveness.

    Authors: Anderson, Mark & Whitcomb, Pat
    Publication: Conference
  5. Graphical Selection of Effects in General Factorials

    October 2007

    Power point presentation demonstrates equivalency of new method with Daniel's half-normal plot of effects for two-level factorials. Demonstrates the general method: two replicates of a 3x2 factorial, two replicates of a 3x2x2 factorial, single replicate of a 3x4x4 factorial.

    Authors: Oehlert, Gary W.; Whitcomb, Patrick J.
    Publication: 2007 Fall Technical Conference
  6. Response Surface Methods for Peak Process Performance

    August 2007

    This is the third article of a series on design of experiments (DOE). The first publication provided tools for process breakthroughs via two-level factorial designs. The second article illustrated how to re-formulate rubbers or plastics using powerful statistical methods for mixture design and analysis. The author now brings the focus back to process improvement and shows how to hit the sweet spot of high yield at lowest possible cost.

    Authors: Anderson, Mark J.; Whitcomb, Patrick J.
    Publication:
  7. Using Graphical Diagnostics to Deal with Bad Data

    January 2007

    This article deals with thorny issues that confront every experimenter, i.e., how to handle individual results that do not appear to fit with the rest of the data - damaging outliers and/or a need for transformation. The trick is to maintain a reasonable balance between two types of errors: (1) deleting data that very only due to common causes, thus introducing bias to the conclusions. (2) not detecting true ouliers that occur due to special causes. Such outliers can obscure real effects or lead to false conclusions. Furthermore, an opportunity may be lost to learn about preventable causes for failure or reproducible conditions leading to break-through improvements (making discoveries more or less by accident).

    Authors: Anderson, Mark J.; Whitcomb, Patrick J.
    Publication: Quality Engineering
  8. Automated Optimization of a Multiplex PCR Using Sagian AAO Software for the Biomek FX Liquid Handling System

    January 2007

    Optimizing biological assays conditions is often a challenging process facing scientists. The demand to produce quality and robust assays that work across a range of biological conditions is often strived for along with a short development timeframe. In addition, automated systems are often required to enable scientists to screen in a high-throughput environment.

    Authors: Campbell, Dana; Fan, Lisa; Roby, Keith; Threadgill, Graham; Whitcomb, Patrick J.
    Publication: Beckman Coulter
  9. Rethink Experiment Design

    November 2006

    This article details the advantages of design of experiments (DOE) over the OFAT (changing only one factor at a time) approach to experimentation. By varying factors at two levels each, but simultaneously rather than one at a time, experimenters can uncover important interactions.

    Authors: Anderson, Mark & Whitcomb, Pat
    Publication: Chemical Processing
  10. Optimisation of EDM Fast Hole Drilling through an Evaluation of Electrode Geometry

    September 2006

    In the application of EDM fast hole drilling, response surface design was used to optimize both drilling and electrode wear.

    Authors: Leao, Fabio; Pashby, Ian; Whitcomb, Patrick J.; Cuttell, Martyn; Lord, Peter
    Publication:
  11. Design of Experiments for Coatings

    January 2006

    The traditional approach to experimentation changes only one process factor at a time (OFAT) or one component in a formulation. However, the OFAT approach does not provide data on interactions of factors (or components), a likely occurrence with coating formulations and processes. Statistically-based design of experiments (DOE) provides validated models, including any significant interactions, that allow you to confidently predict response measures as a function of inputs. The payoff is the identification of "sweet spots," where you can achieve all product specifications and processing objectives.

    Authors: Anderson, Mark J.; Whitcomb, Patrick J.
    Publication: Coatings Technology Handbook
  12. PCR Process Optimized via Split-Plot DOE

    May 2005

    This paper illustrates the use of design of experiments (DOE) and split-plot design to quickly and effectively determine the factor settings that maximize amplification in a polymerase chain reaction (PCR) experiment. As presented at the 2005 ASQ World Congress.

    Authors: Whitcomb, Pat & Kraber, Shari
    Publication: Conference
  13. Response Surface Methods for Process Optimization

    November 2004

    This article explores how optimal experimental design can map performance in the presence of multifactor linear constraints.

    Authors: Anderson, Mark & Whitcomb, Pat
    Publication: Digital Engineering
  14. Interpreting Power in Mixture DOE - Simplified

    July 2004

    Adapted by Mark Anderson from a detailed manuscript by Pat Whitcomb.

    Authors: Anderson, Mark J.; Whitcomb, Patrick J.
    Publication: unpublished
  15. Screening Process Factors In The Presence of Interactions

    May 2004

    This article introduces a new, more efficient type of fractional two-level factorial design of experiments (DOE) tailored for the screening of process factors. These designs are referred to as Min Res IV. Presented at AQC 2004 Toronto.

    Italian translation

    Authors: Anderson, Mark J.; Whitcomb, Patrick J.
    Publication: Conference

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