Probabilistic%20design%20-%20Optimizing%20for%20six%20sigma%20quality

Probabilistic%20design%20-%20Optimizing%20for%20six%20sigma%20quality

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AIAA-2002-1471 Copyright © 2002 by Engineous Software, Inc. 1 Published by the Institute of Aeronautics American Institute of Aeronautics and Astronautics and Astronautics, Inc. with permission. PROBABILISTIC DESIGN: OPTIMIZING FOR SIX SIGMA QUALITY Patrick N. Koch * Engineous Software, Inc. 2000 CentreGreen Way, Suite 100 Cary, North Carolina 27513 ABSTRACT Probabilistic design to address uncertainty and variability has been approached from many different angles, by different communities. While reliability methods focus on probability of constraint satisfaction or violation, robust design methods have focused primarily on the level of performance variation, the sensitivity of design objectives. “Six Sigma” quality concepts have arisen more recently from the manufacturing arena, focusing on measuring and controlling variation. All of these approaches deal with some aspect of modeling uncertainty or variability and measuring, improving, or controlling performance variation. For the case of six sigma, the term “ Design for Six Sigma (DFSS) ” has been coined and is the current push in industry; however, its implementation often does not involve design . In an engineering design context, the concepts and philosophy of Six Sigma can be combined with methods from structural reliability and robust design to formulate a strategy to “ optimize for six sigma” quality. Such a strategy is presented in this paper, a six sigma based probabilistic design optimization formulation. Through combining these concepts and approaches, variability is incorporated within all elements of this probabilistic optimization formulation – input design variable bound formulation, output constraint formulation, and robust objective formulation. This six sigma based probabilistic design optimization formulation, as implemented within the iSIGHT design framework, is demonstrated in this paper for the structural design of a welded joint. Results presented illustrate the trade off between performance and quality when optimizing for six sigma quality. 1 INTRODUCTION Optimization without including uncertainty leads to designs that cannot be called “optimal”, but instead are potentially high risk solutions that likely have a high probability of failing in use. Optimization algorithms tend to push a design towards one or more constraints until the constraints are active, leaving the designer with a design for which even slight uncertainties in the problem formulation or changes in the operating environment could produce failed designs. While most optimization problem formulations and solution strategies are deterministic, very few real engineering problems are void of uncertainty; variation is inherent in material characteristics, loading conditions, simulation model accuracy, geometric properties, manufacturing precision, actual product usage, etc. Traditionally, many uncertainties are removed through assumptions, and others are handled through crude safety factors methods, which often lead to over-designed products
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This note was uploaded on 05/01/2011 for the course MBA MGTN101 taught by Professor Mr.leee during the Spring '11 term at Central Mich..

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Probabilistic%20design%20-%20Optimizing%20for%20six%20sigma%20quality

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