Manufacturing Design and Process Analysis System

• US 20070219657A1
• Filed: 05/11/2007
• Published: 09/20/2007
• Est. Priority Date: 02/04/2002
• Status: Active Grant

• Current Assignee: Steve Tuszynski
• Original Assignee: Steve Tuszynski
• Inventors: Tuszynski, Steve
• Granted Patent: US 7,917,234 B2
• Time in Patent Office: Days
• US Class Current: 700/97
• CPC Class Codes: G05B 15/02 electric
• CPC Class Codes: G05B 19/41865 characterised by job scheduling, process planning, material flow
• CPC Class Codes: G05B 2219/32058 Execute program as function of deviation from predicted state, result
• CPC Class Codes: G06F 2111/08 Probabilistic or stochastic CAD
• CPC Class Codes: G06F 2113/22 Moulding
• CPC Class Codes: G06F 30/00 Computer-aided design [CAD]
• CPC Class Codes: Y02P 90/02 Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

Abstract

Methods, apparatuses and systems that facilitate the design, production and/or measurement tasks associated with manufacturing and other processes. In one embodiment, the present invention provides an understanding of how the multiple characteristics of a given process output are related to each other and to process inputs. This knowledge facilitates a reduction in measurement costs. It also facilitates an understanding of the sometimes complex interrelationships between design targets, design tolerances, process inputs, process control variables, average process output and variation in the process output. As disclosed in more detail below, embodiments of the present invention facilitate 1.) determination of design target values, 2.) determination of design application limits, 3.) design of process inputs, 4.) determination of process control variable settings, and/or 5.) reduction of measurement costs.

Claims

1-92. -92. (canceled)

93. A method facilitating design and manufacturing processes, the method 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 resulting from a process;

evaluating correlations between the plurality of article characteristics;

selecting a predictor characteristic from the plurality of article characteristics based on the correlations between the plurality of article characteristics; and

,

determining the regression model(s) between the predictor characteristic and at least one of the remaining article characteristics in the plurality of article characteristics.

94. The method of claim 93 further comprising

identifying a range for the predictor characteristic over which at least one remaining article characteristic is within design specifications;

qualifying articles resulting from the process based on corresponding values of the predictor characteristic relative to the identified range.

95. The method of claim 93 further comprising

identifying a range for the predictor characteristic over which a plurality of remaining article characteristics are within design specifications;

qualifying articles resulting from the process based on corresponding values of the predictor characteristic relative to the identified range.

96. The method of claim 93 further comprising

idenfying a range for the predictor characteristic over which a plurality of remaining article characteristics are within design specifications;

qualifying articles resulting from the process are within design specifications based on corresponding values of the predictor characteristic relative to the identified range.

97. The method of claim 96 further comprising

determining the respective upper and lower prediction intervals associated with the regression model(s) between the predictor characteristic and at least one of the remaining article characteristics.

98. The method of claim 97 further comprising

receiving lower and upper specifications limits for said at least one of the remaining article characteristics;

locating the lower and upper specification limits associated with said at least one of the remaining article characteristics;

receiving lower and upper specification limits for the predictor characteristic;

locating the upper specification limit associated with the predictor characteristic; and

determining a maximum article characteristic value for the predictor characteristic by selecting the lesser of (1) the upper specification limit for the predictor characteristic and (2) the value of the predictor characteristic at which the upper prediction interval intersects the upper specification limit for said at least one remaining article characteristic.

99. The method of claim 98 further comprising

repeating the determining a maximum article characteristic value step for a desired number of remaining article characteristics in the plurality of article characteristics;

and

determining the most constraining maximum article characteristic value for the predictor characteristic by selecting the lowest maximum article characteristic value.

100. The method of claim 98 further comprising

receiving lower and upper specification limits for at least one of the remaining article characteristics;

locating the lower specification limit associated with the predictor characteristic; and

determining a minimum article characteristic value for the predictor characteristic by selecting the greatest of (1) the lower specification limit for the predictor characteristic and (2) the value of the predictor characteristic at which the lower prediction interval intersects the lower specification limit for said at least one remaining article characteristic.

101. The method of claim 100 further comprising

determining an allowable range for the predictor characteristic subtracting the minimum article characteristic value form the maximum characteristic value.

102. The method of claim 100 further comprising

repeating the determining a minimum article characteristic value step for a desired number remaining article characteristics in the plurality of article characteristics;

and

determining the most constraining minimum article characteristic value for the predictor characteristic by selecting the greatest minimum article characteristic value.

103. The method of claim 102 further comprising:

determining the maximum allowable range for the predictory characteristic by subtracting the most constraining minimum article characteristic value from the most constraining maximum characteristic value

104. The method of claim 103 further comprising

determining the target manufacturing value for the predictor characteristics by selecting a value between the most constraining minimum and maximum article.

105. The method of claim 103 further comprising

determining the target manufacturing value for the predictor characteristic by selecting the midpoint value between the most constraining minimum and maximum values for the predictor characteristic.

106. An apparatus facilitating design, manufacturing, and other processes comprising:

a processor;

a memory;

a software application physically stored in the memory, comprising instructions operable to cause the processor and the apparatus to;

receive a plurality of article characteristic values associated with a set of articles having a range of variation as to a plurality of article characteristics resulting from a process;

evaluate correlations between the plurality of article characteristics;

select a predictor characteristic from the plurality of article characteristics based on the correlations between the plurality of article characteristics; and

determine the regression model(s) between the predictor characteristic and at least one of the remaining article characteristics in the plurality of article characteristics.

107. The apparatus of claim 106 wherein the software application further comprises instructions operable to cause the processor and the apparatus to identify a range for the predictor characteristic over which at least one remaining article characteristic is within design specification.

108. The apparatus of claim 106 wherein the software application further comprises instructions operable to cause the processor and the apparatus to identify a range for the predictor characteristic over which a plurality of remaining article characteristics are within design specifications.

109. The apparatus of claim 106 wherein the software application further comprises instructions operable to cause the processor and the apparatus to identify a range for the predictor characteristic over which a plurality of remaining article characteristics are within design specifications.

110. A computer readable storage medium, for facilitating design, manufacturing, and other processes, comprising instructions operable, when executed, to cause a programmable processor to:

receive a plurality of article characteristic values associated with a set of articles having a range of variation as to a plurality of article characteristics resulting from a process;

evaluate correlations between the plurality of article characteristics;

select a predictor characteristic from the plurality of article characteristics based on the correlations between the plurality of article characteristics; and

determine the regression model(s) between the predictor characteristic and at least one of the remaining article characteristics in the plurality of article characteristics.

111. The computer readable storage medium of claim 110 further comprising instructions operable, when executed, to cause a programmable processor to identify a range for the predictor characteristic over which a plurality of remaining article characteristics are within design specifications.

112. The computer readable storage medium of claim 110 further comprising instructions operable, when executed, to cause a programmable processor to identify a range for the predictor characteristic over which at least one remaining article characteristic is within design specifications.