A Systematic Approach to Planning for a Designed Industrial

I want to study the effect of different tool vendors

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Unformatted text preview: to study the effect of different tool vendors and machine set-up parameters on the dimensional variability of the parts produced on the machines in our CNC-machine center.” Many experimental design applications in industry begin with such a statement. It is well recognized that the planning activities that precede the actual experiment are critical to successful solution of the experimenters’ problem (e.g., see Box, Hunter, and Hunter 1978; Hahn 1977, 1984; Montgomery 1991; Natrella 1979). Montgomery (1991) presented a sevenstep approach for planning experiments, summarized in Table 1. The first three of these steps constitutes the preexperiment planning phase. The detailed, specific activities in this phase are the focus of this article. The emphasis is planning for a screening ex- periment, or a step in sequential experimentation on an existing product/process, off-line or on-line. Many of the issues addressed, however, also apply to new products/processes or research and development (R&D) and to various additional experimental goals, such as optimization and robustness studies. 1.2 A Gap It is often said that no experiment goes exactly as planned, and this is true of most industrial experiments. Why? One reason is that statisticians who design experiments with scientists and engineers (the “experimenters”) usually have to bridge a gap in knowledge and experience. The consequences of not bridging this gap can be serious. The statistician’s lack of domain knowledge can lead to: 1. Unwarranted assumptions of process stability during experimentation 2. Undesirable combinations of control-variable levels in the design 3. Violation or lack of exploitation of known physical laws 4. Unreasonably large or small designs 2 DAVID E. COLEMAN AND DOUGLAS Table 1. Steps of Experimentation 1. 2.” 3.* 4. 5. 6. 7. Recognition of and statement of the problem Choice of factors and levels Selection of the response variable(s) Choice of experimental design Conduction of the experiment Data analysis Conclusions and recommendations *In some situations, steps 2 and 3 can be revers...
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