Design of Experiments (DOE) is also offered to as Designed Experiments or Experimental Design – all Of the terms have the same meaning. Experimental design can be used at the point of greatest leverage to reduce design costs by speeding up the design process, reducing late engineering design changes, and reducing product material and labor complexity. Designed Experiments are also powerful tools to achieve manufacturing cost savings by minimizing process variation and reducing rework, scrap, and the need for inspection.This Toolbox module includes a general overview of Experimental Design and links and other resources to assist o in conducting designed experiments. A glossary of terms is also available at any time through the Help function, and we recommend that you read through it to familiarize yourself with any unfamiliar terms. 2.
Preparation If you do not have a general knowledge of statistics, review the Histogram, Statistical Process Control, and Regression and Correlation Analysis modules of the Toolbox prior to working with this module.You can use the Marmoset’s data analysis software [email protected] Excel to create and analyze many commonly used but powerful experimental designs. Bare trials of several other statistical packages can also be downloaded through the Marmosets. Com Statistical Software module of the Toolbox. In addition, the book DOE Simplified, by Anderson and Whitlock, comes with a sample of excellent DOE software that Will work for 180 days after installation. 3.Components Of Experimental Design Consider the following diagram of a cake-baking process (Figure 1).
There are three aspects Of the process that are analyzed by a designed experiment: Factors, or inputs to the process. Factors can be classified as either controllable r uncontrollable variables. In this case, the controllable factors are the ingredients for the cake and the oven that the cake is baked in. The controllable variables will be referred to throughout the material as factors.
Note that the ingredients list was shortened for this example – there could be many other ingredients that have a significant bearing on the end result (oil, water, flavoring, etc). Likewise, there could be other types of factors, such as the mixing method or tools, the sequence of mixing, or even the people involved. People are nearly considered a Noise Factor (see the glossary) – an uncontrollable factor that causes variability under normal operating conditions, but we can control it during the experiment using blocking and randomization.Potential factors can be categorized using the Fishbone Chart (Cause ; Effect Diagram) available from the Toolbox.
Levels, or settings of each factor in the study. Examples include the oven temperature setting and the particular amounts of sugar, flour, and eggs chosen for evaluation. Response, or output of the experiment. In the case of cake baking, the taste, consistency, and appearance of the cake are measurable outcomes potentially influenced by the factors and their respective levels.Experimenters often desire to avoid optimizing the process for one response at the expense Of another.
Gore this reason, important outcomes are measured and analyzed to determine the factors and their settings that Will provide the best overall outcome for the critical-to-quality characteristics – both measurable variables and assessable attributes. Figure 1 unaffiliated topics 4. Purpose of Experimentation Designed experiments have many potential uses in improving processes and products, including: Comparing Alternatives.In the case of our cake-baking example, we might want to compare the results from two different types of flour.
If it turned out that the flour from different vendors was not significant, we could select the lowest-cost vendor. If flour were significant, then we would select the best flour. The experiment(s) should allow us to make an informed decision that evaluates both quality and cost.
Identifying the Significant Inputs (Factors) Affecting an Output (Response) – separating the vital few from the trivial many.