Method for improving a manufacturing process by conducting a full factorial experiment to optimize process variable settings

A method of improving a manufacturing process wherein a target is determined for a measurable response, the target being indicative of an improvement in the process, the method comprising:

• conducting a full factorial experiment with at least two process variables being adjusted between at least two level settings with output responses being measurements of the response for which the target is determined;
• receiving the responses of the full factorial experiment and using the responses to calculate individual contrasts for each process variable and each interaction among the process variables and displaying each of the contrasts in a document at a particular location of the document, with a notation corresponding to the particular location, the notation indicating the level settings of the other of the process variables not involved in the particular contrasts;
• adding the individual contrasts of each process variable and each interaction to generate separate contrast sums;
• selecting at least one of the contrast sums when it is greater than at least one of the other contrast sums by a pre defined factor;
• and
• adjusting the level settings of the process variables using an estimated effect associated with the selected contrast sum, wherein the level settings of the process are adjusted using the estimated effect only if the estimated effect is determined to be significant, wherein determining the significance of the effect comprises sorting the responses and checking an end count.

A method of manufacturing wherein there is a required target for a measurable response of the process, the method comprising:

• conducting a full factorial experiment by setting a plurality of process variables of the process at a plurality of settings, in a plurality of combinations of settings, and receiving at least one measurement of the response of the process for each combination of level settings;
• receiving the responses of the full factorial experiment and using the responses to calculate individual contrasts for each process variable and each interaction among the process variables and displaying each of the contrasts in a display at a particular location of the display, with a notation corresponding to the particular location, the notation indicating the level settings of the other of the process variables not involved in the particular contrasts;
• adding the individual contrasts of each process variable and each interaction to generate separate contrast sums;
• calculating effects estimates for each of the contrast sums;
• identifying contrast sums that are greater than at least one of the other contrast sums;
• verifying that both variables of an estimated interaction effect must be set at the levels of the interaction to impart an effect substantially equal to the effect of the interaction by evaluating the variance of the contrasts displayed when the sum of contrasts for that interaction has been identified;
• and
• adjusting the level settings of the process variables using at least one of the estimated effects;
• conducting at least two trials for the full factorial experiment;
• and
• assuming and displaying at least one hypothetical additional process variable and substituting one set of trial responses as responses for the hypothetical variable and calculating individual contrasts for the hypothetical factor including the interaction contrasts thereof, whereby replicate effects are calculated.

A method of manufacturing wherein there is a required target for a measurable response of the process, the method comprising:

• conducting a fall factorial experiment by setting a plurality of process variables of the process at a plurality of settings, in a plurality of combinations of settings, and receiving at least one measurement of the response of the process for each combination of level settings;
• receiving the responses of the full factorial experiment and using the responses to calculate individual contrasts for each process variable and each interaction among the process variables and displaying each of the contrasts in a display at a particular location of the display, with a notation corresponding to the particular location, the notation indicating the level settings of the other of the process variables not involved in the particular contrasts;
• adding the individual contrasts of each process variable and each interaction to generate separate contrast sums;
• calculating effects estimates for each of the contrast sums;
• identifying contrast sums that are greater than at least one of the other contrast sums;
• verifying that both variables of an estimated interaction effect must be set at the levels of the interaction to impart an effect substantially equal to the effect of the interaction by evaluating the variance of the contrasts displayed when the sum of contrasts for that interaction has been identified;
• and
• adjusting the level settings of the process variables using at least one of the estimated effects, where the level settings of the process variables are adjusted using the estimated effect only when the estimated effect has been tested and found to be significant, wherein testing the significance of an estimated effect comprises removing an effect previously found to be significant, sorting the responses, and checking an end count.