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Design for Six Sigma Solution

Probabilistic Characterization Probabilistic Characterization quantifies the reliability or quality of the product by means of a statistical analysis.Proba- bilistic characterization combines the deterministic characterization, either handbook or FEA analysis, with statistical analysis tools to address the effect of statistical variability and uncertainty influ- encing the products behavior. Probabilistic analysis typically involves four areas of statistical variability:the geometric shape, the material properties, the loading and the boundary conditions. For example, the statistical variability of the geometry of a product would try to capture the product-to-product differ- ences due to manufacturing imperfections quantified by the manufacturing tolerances.Because the statistical analy- sis typically requires many data points, a combination of computer-based FEA analysis with statistical analysis is the most time- and cost-efficient method in practice.Unlike the first two methods, probabilistic characterization provides a probability of success or failure and not just a simple yes-no evaluation.For instance, a probabilistic analysis could determine that one part in 1,000,000 would fail or what the probability is of a product surviving its expected useful life. Deterministic Characterization Deterministic Characterization typically refers to an analysis of the product with- out testing it.This analysis could range from simple engineering handbook calculations to elaborate finite element analyses (FEA).Once again, the typical judgment is of a yes-no nature.

Product Behavior Quantify the quality of products with ANSYS

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DesignXplorer

TM Characterizing the behavior of a product (a part or an assembly of parts) under oper- ational conditions can be done in three ways:

Design for Six Sigma designs quality into a product. By assessing the variations that a product experiences during manufacture and use, it is possible to make a product that performs its intendedfunction regardless of these variations; such a product is “robust,”andtherefore, Design for Six Sigmais sometimes called Robust Design.Design for Six Sigma (DFSS) is an analysis technique to determine the extent to which uncertaintiesin the model affect the results of an analysis. Based on a probabilistic characterization, Design for SixSigma enables users to quantify the quality of a product by addressing issues such as minimizing warranty costs and assessing the reliability of the product. DFSS goes one step further than a proba-bilistic characterization by allowing users to optimize design variables to achieve a particular proba-bilistic result such as Six Sigma, which, including long-term effects, is 3.4 failures in one million parts! Six Sigma initiatives try to optimize the manufacturing process such that it automatically producesparts conforming to Six Sigma quality. In contrast, DFSS optimizes the design itself such that the partconforms to Six Sigma quality even with variations in manufacturing. For Design for Six Sigma andRobust Design, quality is an explicit goal of the optimization.The ANSYS

Empirical Characterization Empirical Characterization refers to creating a prototype (or prototypes) of the product hopefully replicating the manufac- turing steps that are to be used in a pro- duction run of the product.The product is then tested to determine its behavior and to make final judgment on whether the product will perform successfully in the field.Typically this judgment is of a yes-no nature.In other words, the product will fail or not;hence its design is acceptable or not.
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DesignXplorer VT

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and DesignXplorer solutions provide users with the ability to create a Robust Design by allowing the user to define both Design Variables and Uncertainty Variables,and then optimize a set of reliability goals for quantities such as fatigue life, stress or deflection.

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