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5. Process Improvement
5.5. Advanced topics
5.5.9. An EDA approach to experimental design DOE contour plot

How to Interpret: Optimal Setting

Optimal setting The "near-point" optimality setting is the intersection of the steepest-ascent line with the optimal setting curve.

Theoretically, any (X1, X3) setting along the optimal curve would generate the desired response of Y = 100. In practice, however, this is true only if our estimated contour surface is identical to "nature's" response surface. In reality, the plotted contour curves are truth estimates based on the available (and "noisy") n = 8 data values. We are confident of the contour curves in the vicinity of the data points (the four corner points on the chart), but as we move away from the corner points, our confidence in the contour curves decreases. Thus the point on the Y = 100 optimal response curve that is "most likely" to be valid is the one that is closest to a corner point. Our objective then is to locate that "near-point".

Defective springs example In terms of the defective springs contour plot, we draw a line from the best corner, (+, +), outward and perpendicular to the Y = 90, Y = 95, and Y = 100 contour curves. The Y = 100 intersection yields the "nearest point" on the optimal response curve.

Having done so, it is of interest to note the coordinates of that optimal setting. In this case, from the graph, that setting is (in coded units) approximately at

    (X1 = 1.5, X3 = 1.3)
Table of coded and uncoded factors With the determination of this setting, we have thus, in theory, formally completed our original task. In practice, however, more needs to be done. We need to know "What is this optimal setting, not just in the coded units, but also in the original (uncoded) units"? That is, what does (X1=1.5, X3=1.3) correspond to in the units of the original data?

To deduce his, we need to refer back to the original (uncoded) factors in this problem. They were:

    Uncoded Factor
    X1 OT: Oven Temperature
    X2 CC: Carbon Concentration
    X3 QT: Quench Temperature
Uncoded and coded factor settings These factors had settings-- what were the settings of the coded and uncoded factors? From the original description of the problem, the uncoded factor settings were:
  1. Oven Temperature (1450 and 1600 degrees)
  2. Carbon Concentration (0.5 % and 0.7 %)
  3. Quench Temperature (70 and 120 degrees)
with the usual settings for the corresponding coded factors:
  1. X1 (-1, +1)
  2. X2 (-1, +1)
  3. X3 (-1, +1)
Diagram To determine the corresponding setting for (X1=1.5, X3=1.3), we thus refer to the following diagram, which mimics a scatter plot of response averages--oven temperature (OT) on the horizontal axis and quench temperature (QT) on the vertical axis:

diagram representing response averages at optimal contour

The "X" on the chart represents the "near point" setting on the optimal curve.

Optimal setting for X1 (oven temperature) To determine what "X" is in uncoded units, we note (from the graph) that a linear transformation between OT and X1 as defined by
    OT = 1450 => X1 = -1
    OT = 1600 => X1 = +1
    X1 = 0 being at OT = (1450 + 1600) / 2 = 1525
X1:       -1             0            +1
OT:      1450          1525          1600
and so X1 = +2, say, would be at oven temperature OT = 1675:
X1:       -1             0            +1            +2
OT:      1450          1525          1600          1675
and hence the optimal X1 setting of 1.5 must be at
    OT = 1600 + 0.5*(1675-1600) = 1637.5
Optimal setting for X3 (quench temperature) Similarly, from the graph we note that a linear transformation between quench temperature QT and coded factor X3 as specified by
    QT = 70 => X3 = -1
    QT = 120 => X3 = +1
    X3 = 0 being at QT = (70 + 120) / 2 = 95
as in
X3:    -1             0            +1
QT:    70            95           120
and so X3 = +2, say, would be quench temperature = 145:
X3:    -1             0            +1            +2
QT:    70            95           120           145
Hence, the optimal X3 setting of 1.3 must be at
    QT = 120 + 0.3*(145-120)
    QT = 127.5
Summary of optimal settings In summary, the optimal setting is
    coded : (X1 = +1.5, X3 = +1.3)
    uncoded: (OT = 1637.5 degrees, QT = 127.5 degrees)
and finally, including the best setting of the fixed X2 factor (carbon concentration CC) of X2 = -1 (CC = 0.5 %), we thus have the final, complete recommended optimal settings for all three factors:
    coded : (X1 = +1.5, X2 = -1.0, X3 = +1.3)
    uncoded: (OT = 1637.5, CC = 0.7 %, QT = 127.5)
If we were to run another experiment, this is the point (based on the data) that we would set oven temperature, carbon concentration, and quench temperature with the hope/goal of achieving 100 % acceptable springs.
Options for next step In practice, we could either
  1. collect a single data point (if money and time are an issue) at this recommended setting and see how close to 100 % we achieve, or

  2. collect two, or preferably three, (if money and time are less of an issue) replicates at the center point (recommended setting).

  3. if money and time are not an issue, run a 22 full factorial design with center point. The design is centered on the optimal setting (X1 = +1, 5, X3 = +1.3) with one overlapping new corner point at (X1 = +1, X3 = +1) and with new corner points at (X1, X3) = (+1, +1), (+2, +1), (+1, +1.6), (+2, +1.6). Of these four new corner points, the point (+1, +1) has the advantage that it overlaps with a corner point of the original design.
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