Data processing: structural design – modeling – simulation – and em – Modeling by mathematical expression
Reexamination Certificate
1999-08-23
2003-05-27
Broda, Samuel (Department: 2123)
Data processing: structural design, modeling, simulation, and em
Modeling by mathematical expression
C703S007000, C703S022000, C702S034000, C700S108000
Reexamination Certificate
active
06571202
ABSTRACT:
BACKGROUND OF THE INVENTION
The invention relates to a method for applying design for reliability into design for Six Sigma.
Defect levels in the design and manufacturing of products must be kept as low as possible. One measure of defect levels is “Six Sigma” engineering and manufacturing. Under the “Six Sigma” paradigm defect levels are kept below 3.4 parts per million. This means that at least 999,996.6 out of every million opportunities must be completed successfully within specification.
Meeting the demands of the “Six Sigma” paradigm requires a concurrent design and manufacturing engineering that achieves robust product design and manufacturing processes. The product design must be robust to natural sources of variation, and the manufacturing process must implement process controls that keep manufacturing within specification.
Creation of designs and processes that synergistically interact to meet “Six Sigma” requirements are described, for example, in Mikel J. Harry,
The Vision of Six Sigma: A Roadmap for Breakthrough, Sigma Publishing Co.,
1994. One early application of “Six Sigma” was in mechanical tolerancing. Mechanical tolerancing is the determination of the zone over which the individual component mechanical parameters of the components in an assembly can fluctuate from the nominal values thereof and still yield an acceptable assembly.
Six sigma design techniques are now available for design and production processes. Analogous procedures for reliability during customer or field use are needed. What is different about reliability (quality over time) is that data often involve time to failure rather than part measurements. Weibull, exponential and/or lognormal distributions (or more complex models) for time to failure are generally required in place of the normal distribution. Data often include runouts (units which have not failed). Current design for Six Sigma techniques include methods for handling parts, processes, performance, and software, but provide no method for handling reliability. There is a need for a process that closes this gap and allows reliability to be included in Six Sigma engineering projects.
The use of Monte Carlo Analysis in component tolerancing is described in, for example, Gerald J. Hahn & Samuel S. Shapiro, Statistical Models in Engineering, John Wiley and Sons, Inc., 1967, pages 236-257.
Monte Carlo analysis is performed by first establishing a range for each individual component tolerance, for example a range of Upper Specification Limit-Lower Specification Limit (USL-LSL). Then a random sampling fitting a mathematically defined distribution is taken from within this range, and the response evaluated. The output values are analyzed by traditional statistical methods.
Monte Carlo analysis uses a random number generator to perform the distribution sampling. Therefore, Monte Carlo simulation can simulate large sample sizes on digital computers. Monte Carlo analysis is especially useful where complex assemblies can not be readily or realistically analyzed by linear methods as root-sum-of-squares analysis or worst case analysis. Monte Carlo analysis is also useful where the completed assemblies are costly or time consuming to manufacture.
Monte Carlo techniques and the Six Sigma paradigm are disclosed in U.S. Pat. No. 5,301,118 issued on Apr. 5, 1994 to Heck et al.
SUMMARY OF THE INVENTION
A method for applying design for reliability into design for Six Sigma is described. The method includes establishing an appropriate model for reliability as a function of time; determining a reliability transfer function; calculating defects per opportunity per unit of time; entering said defects per opportunity per unit of time into a calculation of value of sigma; selecting one or more noise factors likely to have an impact on reliability; and performing either a closed form analytical solution of said impact on reliability or using a Monte Carlo analysis to determine the impact.
A storage medium is encoded with machine-readable computer program for applying design for reliability into design for Six Sigma method described above. The storage medium includes instructions for causing a computer to implement the method.
These and other features and advantages of the present invention will be apparent from the following brief description of the drawings, detailed description, and appended claims and drawings.
REFERENCES:
patent: 5301118 (1994-04-01), Heck et al.
patent: 5418931 (1995-05-01), Moorby
patent: 5452218 (1995-09-01), Tucker et al.
patent: 5539652 (1996-07-01), Tegethoff
patent: 5581466 (1996-12-01), Van Wyk et al.
patent: 5956251 (1999-09-01), Atkinson et al.
patent: 6226597 (2001-05-01), Eastman et al.
patent: 6253115 (2001-06-01), Martin et al.
Chen et al, “A Procedure for Robust Design: Minimizing Variations Caused by Noise Factors and Control Factors”, Journal of Mechanical Design, Transactions of the ASME, vol. 118 No. 4, pp. 479-489 (1996).*
Hsieh et al, “A Framework of Integrated Reliability Demonstration in System Development”, IEEE Proceedings of the Reliability and Maintainability Symposium, pp. 258-264 (Jan. 1999).*
Thomas et al, “Devising a Test Strategy to Characterize System Reliability of Submicron Interconnect Wiring”, IEEE Third International Workshop on Statistical Metrology, pp. 82-87 (Jun. 1998).*
Smith et al, “Worst Case Circuit Analysis—An Overview (Electronic Parts/Circuits Tolerance Analysis)”, 1996 IEEE Proceeding of Reliability and Maintainability Symposium, pp. 326-334 (Jan. 1996).*
Hasnat et al, “A Manufacturing Sensitivity Analysis of 0.35 &mgr;m LDD MOSFET's”, IEEE Transactions on Semiconductor Manufacturing, vol. 7 Issue 1, pp. 53-59 (Feb. 1994).*
MJ Harry, “The Vision of Six Sigma: A Roadmap for Breakthrough”, Sigma Publishing Co., 1994, pp. 2.2-2.8, 10.17-10.21, 22.18-22.20.
W. Nelson, “Accelerated Testing: Statistical Models, Test Plans, And Data Analyses”, 1990, pp. 1-53.
GJ Hahn, et al, “Statistical Models in Engineering”, John Wiley & Sons, Inc., 1967, pp. 236-257.
Doganaksoy Necip
Hahn Gerald John
Hasan Omar Aquib
Hauer Thomas Anthony
Loman James Mark
Broda Samuel
General Electric Company
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