A GOAL-ORIENTED, INVERSE DECISION-BASED DESIGN METHOD FOR MULTI-COMPONENT PRODUCT DESIGN

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AbstractMethodologyMaterial ModelInput DataResultsAcknowledgmentsReferences

Abstract

We design two components of an American football helmet (composite shell and foam liner) to illustrate the efficacy of a goal-oriented, inverse decision-based design method for multi-component product design. The method is goal-oriented because we first identify the end performance goals for the helmet: to dissipate impact energy, mitigate stress waves, and minimize system weight. We arrange the components in the order that they receive impact energy and then find satisficing solutions for the foam liner that achieve the system-level goals as close as possible, and then we adjust the targets for the composite shell to reduce the system weight without a substantial loss in performance. In our method, we use the Concept Exploration Framework to systematically gather information about each component, and the compromise Decision Support Problem to generate satisficing solutions under uncertainty. Finally, we verify our design decisions with Finite Element Analysis. Although the results are interesting, our focus is to establish the efficacy of the inverse method for the design of an American football helmet.

Author(s): Tate R. Fonville, Anand B. Nellippallil, Mark F. Horstemeyer, Janet K. Allen, Farrokh Mistree

Corresponding Author: Janet K. Allen (janet.allen@ou.edu)


File:Football Helmet GoID.tiff

File:ConceptExplorationFramework.tiff

Methodology

In this work, we demonstrate the design of two American football helmet components with a proposed goal-oriented, inverse decision-based design method for multi-component product design. The method shown in Figure 1 requires four essential steps. The first step in the method shown in Figure 1 is to establish a forward flow of information that links each component to the system-level goals in a consistent way. To establish the “forward modeling and information flow” (shown by the thick blue arrow) we track the energy transferred from a helmet impact, through the composite shell, through the foam liner, and then into the player’s head. The green dashed arrow represents soft information about a component, to be determined in the design analysis. Solid green arrows represent our known design information, or “hard information.” The foam liner and composite shell differ greatly in form and function, however must be represented in such a way that ensures a proper flow of information. To do this, we create metamodels that describe the system-level goals in terms of the individual component design variables. We identify three system-level goals, namely, internal energy, system weight, and impulse where we desire to maximize the first goal and minimize the last two. With a proper forward flow of information, we can find satisficing solutions for the first component (foam liner), and then pass hard information back to design the next component (composite shell) in an inverse fashion.

In Step 2, we use the Concept Exploration Framework (CEF) [1] (Figure 2) to systematically gather information about a helmet component, and then identify design alternatives to explore and find satisficing design solutions under uncertainty. We use the compromise Decision Support Problem (cDSP) [2] to find satisficing solutions for the foam liner that achieve the 3 system-level goals as close as possible. All the design information going into the analysis at this point, represented by the dashed green arrows, is soft information. After the analysis, we verify the design decisions with FEA, and then modify our design goals, constraints, and targets. In Step 3, we pass hard information back to design the composite shell in a similar fashion as in Step 2, but with respect to modified design goals. In Step 4 we verify all design solutions with FEA.

Material Model

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Input Data

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Results

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Acknowledgments

JKA and FM gratefully acknowledge financial support from the John and Mary Moore Chairs and L.A. Comp Chair respectively at the University of Oklahoma. TF and MFH thank the Center for Advanced Vehicular Systems (CAVS) at Mississippi State University for supporting this effort.

References

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  1. Nellippallil, A. B., Rangaraj, V., Gautham, B. P., Singh, A. K., Allen, J. K., and Mistree, F., 2018, "An Inverse, Decision-Based Design Method for Integrated Design Exploration of Materials, Products and Manufacturing Processes," Journal of Mechanical Design, vol. 140, no. 11, pp. 111403.
  2. Mistree, F., Hughes, O. F., and Bras, B., 1993, "Compromise Decision Support Problem and the Adaptive Linear Programming Algorithm," Progress in Astronautics and Aeronautics, vol. 150, pp. 251-251.
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