Analyse your Results (Graph Columns)

To help determine whether there are any individual out of specification results for each response (i.e., results beyond that considered practically acceptable) generated by the robustness study, click on Graph Columns under the Design node on the left-hand tree structure

Select Run from the X axis list and cycle through all the responses in the Y axis list. You can Colour By each response to highlight results from low-to-high values.

Check the range & pattern of response values against the response goal, target or specification. Also check the background variation – differences between the repeat centre point results – is not exceesive. If the results fall within acceptable ranges (specification) this would suggest the process or method is able to comply with acceptable quality over the preferred operational ranges and for all combinations of the parameters investigated. To ensure that the process or method is able to produce results that conform to specifications during normal usage and allowing for variation you should also consider the Point Prediction results generated at the extreme settings of the method parameters

Use the run order to display the results, so they are in the same order the experiments were conducted in – this gives you information about possible systematic trends in the data parameters

Analyse your Results (Effects, ANOVA and Model Graphs)

Under the Analysis node on the left-hand tree structure, click on each response you want to analyse and simply work your way along the analysis buttons from left-to- right. Please refer to the Tipsheet on 2-Level Designs for more details on analysing these types of designs.

For robustness studies the key output to focus on are the Effects Plots. The Half-Normal plot and Pareto Chart provide a visual means of assessing robustness. On the former plot determine whether the effects lie to the right of the line and away from the green triangles, which represent differences between the replicated points or background noise, while the Pareto Chart provides statistical thresholds to test the significance of the effects selected. If there are no statistically significant effects for each of the responses – in other words the effects are no larger than the noise – then the process or method is robust.

For Hardness, since all the effects lie approximately on a straight line and the size of effects are not statistically significant, or unimportant relative to the noise which stretches across the plot, this response is robust to changes in the factor ranges.

Note also that none of the individual results are out of specification (i.e., < 11kp) according to the Graph Columns plot for Hardness above

For Compressibility, there are two strong statistically significant effects due to C-Inlet Air Temperature and D-Spray Rate.

According to the Graph Columns plot the individual results are all within specification (i.e., < 18%), so the process may well be practically robust. It is worth looking at the Model Graphs for the two statistically significant effects to determine whether tighter control or tolerances on these parameters are required