# simProc: Simulated Process In qualityTools: Statistical Methods for Quality Science

## Description

This is a function to simulate a black box process for teaching the use of designed experiments. The optimal factor settings can be found using a sequential assembly strategy i.e. apply a 2^k factorial design first, calculate the path of the steepest ascent, again apply a 2^k factorial design and augment a star portion to find the optimal factor settings. Of course other strategies are possible.

## Usage

 `1` ```simProc(x1, x2, x3, noise = TRUE) ```

## Arguments

 `x1` numeric vector containing the values for factor 1. `x2` numeric vector containing the values for factor 2. `x3` numeric vector containing the values for factor 3. `noise` logical value deciding whether noise should be added or not. Default setting is ‘FALSE’.

## Details

factor 1 is best within [40, 250]; factor 2 within [90, 240]

## Value

`simProc` returns a numeric value within the range [0,1].

## Note

For an example in context which shows the usage of the function `simProc()` please read the vignette for the package `qualityTools` at http://www.r-qualitytools.org/html/Improve.html

## Author(s)

Thomas Roth [email protected]

`facDesign` for 2^k factorial designs
`rsmDesign` for response surface designs
`fracDesign` for fractional factorial design
 ```1 2 3``` ```simProc(120, 140, 1) simProc(120, 220, 1) simProc(160, 140, 1) ```