Manufacturing design and process analysis system

A method facilitating a determination of the magnitude and direction by which a pre-process characteristic would have to be adjusted to achieve a given design target, comprising

• receiving a plurality of article characteristic values associated with a set of articles having a range of variation as to a plurality of article characteristics;
• selecting a predictor characteristic from the plurality of article characteristics;
• determining the regression model between the predictor characteristic and a first remaining article characteristic in the plurality of article characteristics,
• receiving the target values for the predictor characteristic and the first remaining article characteristic;
• computing, based on the regression model, the value of the first remaining article characteristic at the target value of the predictor characteristic;
• determining the magnitude and direction of the offset for the first remaining article characteristic by computing the difference between the computed value of the first remaining article characteristic and the target value of the first remaining article characteristic;
• storing the magnitude and direction of the offset in a data structure in association with an identifier for the first remaining article characteristic;
• and
• repeating the computing, determining and storing steps for all desired remaining characteristics.

A method facilitating analysis of the achievable gains in operating range associated with relaxing design tolerances corresponding to at least one article characteristic, comprising

• receiving a plurality of article characteristic values associated with a set of articles having a range of variation as to a plurality of article characteristics;
• selecting a predictor characteristic from the plurality of article characteristics;
• determining the regression model between the predictor characteristic and a first remaining article characteristic in the plurality of article characteristics, wherein the regression model includes lower and upper prediction boundaries;
• receiving lower and upper specification limits for the predictor characteristic and the first remaining article characteristic;
• computing, based on the regression model, the minimum and maximum predictor characteristic values at which the first remaining article characteristic remains within the lower and upper specification limits of the first remaining article characteristic;
• repeating the determining, receiving, and computing steps for all desired remaining article characteristics;
• creating a most constraining minimum predictor characteristic list by ranking the remaining article characteristics by the respective minimum predictor characteristic values associated therewith;
• and
• starting with the remaining article characteristic associated with the greatest minimum predictor characteristic value:
• computing the individual gain in operating range achieved by relaxing the applicable specification limit of the remaining article characteristic to the value corresponding to the minimum predictor characteristic value associated with the next remaining article characteristic in the ranked list;
• computing the cumulative gain associated with relaxing the applicable specification limit of the corresponding article characteristic;
• and
• repeating the first and second computing steps for all desired remaining article characteristics.