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Why should I use Design of Experiments (DoE) in a manufacturing context?


Frequently Asked Questions

All processes used in the conversion activities found in manufacturing organisations are subject to variation. The sources of variation are due to combinations of materials, equipment, method and environmental conditions. All of these occur naturally and must be accommodated in the process design if the desired output characteristics are to be obtained reliably.

Many manufacturing organisations will be tempted to start to run their process with variable settings that apparently deliver the desired output characteristic without fully exploring the range of settings for each variable that can be tolerated. Equally the impact of natural changes to the process inputs is often neglected.

Most process control tools require that the process be adjusted to accommodate natural changes to input variables, however such changes, for example in response to natural batch to batch material property variations, may result in a sudden breakdown of the process. This is obviously a potentially costly situation.

During the set-up of new manufacturing processes it is therefore important to understand the impact of the variables that are used to achieve optimum performance and to understand the effect on the output of natural variation.

With four or more variables, understanding the impact of altering variable values becomes complex, particularly if the effects of interactions between variables are considered. Interaction effects are often more significant to output characteristics than single variable effects.

Design of Experiments (DoE) enables this complex situation to be understood, thus gaining an in-depth knowledge of the process. This in turn can direct the engineering team to select the right control variables and allowable ranges for the setting and adjustment of those variables.

Using DoE techniques it is also possible to fully optimise the process and to highlight areas where material or equipment improvements could offer the most benefit.

Several models for DoE are available, including Full Factorial, Fractional Factorial and Taguchi approaches. TWI can assist members in defining their processes by choosing the appropriate DoE methodology.

For more information, please contact us.

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