This model demonstrates how @RISK can be used to ensure the quality of experimental design. Here we examine a metallic burst cup manufactured by welding a disk onto a ring. The product functions as a seal and a safety device, so it must hold pressure in normal use, and it must separate if the internal pressure exceeds the safety limit. We need to make sure that the cup's strength falls within specified limits. There are a number of uncertain factors in both the material used to manufacture the cup and the manufacturing process itself which may affect the resulting strength.

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Explanation

This model was fabricated to demonstrate the use of the @RISK Monte Carlo simulation capability combined with experimental design. The part under investigation is a metallic burst cup manufactured by welding a disk onto a ring. (See Figure 1) The product functions as a seal and a safety device, so it must hold pressure in normal use, and it must separate if the internal pressure exceeds the safety limit.

The model relates the weld strength to process and design factors, models the variation for each factor, and forecasts the product performance in relation to the engineering specifications. Modeling a response based on multiple factors can often be accomplished by generating a statistically significant function through experimental design or multiple regression analysis.

In this example, @RISK simulates the variation using normal distributions for each factor. @RISK distribution assignment supports cell referencing so that you can easily set-up a tabular model that can be updated throughout a product and process development lifecycle. The Process Performance output section was generated using @RISK functions.

The @RISK output distribution displays the expected performance based on the design and process input variation. You can easily access the output statistics using the reporting features or through @RISK functions.

The @RISK Sensitivity Analysis clearly shows that the Weld Time and Amplitude parameters are driving the weld strength variation.

The next steps for this problem could include two options: The Engineer can attempt to reduce or better control the variation within the Weld Time and Amplitude, or use RISKOptimizer to find the optimal process and design targets to maximise yield or reduce scrap cost.

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