By Stefan H. Steiner
Decreasing the adaptation in strategy outputs is a key a part of procedure development. For mass produced parts and assemblies, lowering version can at the same time decrease total expense, increase functionality and bring up patron delight with the product.
The authors have based this publication round an set of rules for decreasing method version that they name "Statistical Engineering." The set of rules is designed to unravel continual difficulties on present excessive to medium quantity production and meeting methods. the basic foundation for the set of rules is the assumption that we are going to observe in your price range adjustments to the method that would lessen edition if we elevate our wisdom of ways and why a procedure behaves because it does. A key solution to raise approach wisdom is to benefit empirically, that's, to profit by way of statement and experimentation.
The authors speak about intimately a framework for making plans and interpreting empirical investigations, recognized via its acronym QPDAC (Question, Plan, facts, research, Conclusion). They classify all powerful how one can lessen version into seven ways. a special point of the set of rules forces early attention of the feasibility of every of the ways.
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Extra info for Statistical engineering : an algorithm for reducing variation in manufacturing processes
Example text
These suspected causes of frost buildup were not under the control of the manufacturer. They dominated the effects of inputs that changed during manufacturing and assembly. Many problems have known or identified causes. In an automotive painting operation, process engineers discovered that a visual defect on the roof of the car could be eliminated by reducing the thickness (film build) of the clear coat, the last layer applied in the process. qxd 10 3/31/05 10:30 AM Page 10 Chapter One: Introduction However, there was a heavy price to pay for this supposed solution due to poorer overall appearance of the painted surface.
4 For us, the key issue is the size of the effects. We want to find dominant causes that contribute substantially to the variation in the output. 3 DISPLAYING AND QUANTIFYING PROCESS VARIATION We will need to quantify process variation at several stages in the variation reduction algorithm. For example, we may set the goal to reduce the variation by 50%. We require a numerical measure of variation to define this goal explicitly. We use the camshaft manufacturing process described in Chapter 1 to illustrate.
A painting process may produce visual defects such as dirt, craters, and so forth. A molding process can generate defects such as porosity, shrinks, or inclusions; an assembly process can deliver parts that fail to function; and so on. In all these examples, a part either has the defect or it does not. The target value is no defect. The goal of the project is to reduce the frequency of the defect—that is, to reduce the variation in the output by making more parts without defects. Problems defined in terms of a binary output rather than a continuous output can be difficult because it is harder to learn about the process.
