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Thursday, June 28, 2007 - 9:00 AM
MDI5.2

Proabilistic Property Prediction of Ultra High Strength Corrosion-Resistant Steel

J. W. Jung, B. Tufts, QuesTek Innovations LLC, Evanston, IL

As the first prospective application of the Accelerated Insertion of Materials (AIM) technology, QuesTek Innovations achieved the validation and adoption of Ferrium S53 in landing gear. The goal was to produce a mechanistic-based method for estimating property variation with calibration by minimal data sets. The Metallic Materials Properties Development and Standardization handbook ultimately requires 100 measurements of yield and ultimate tensile strength from 10 heats of material to establish A-basis minimum design properties. The AIM procedure estimates these from science-based modeling using only an S-basis data set of 30 measurements collected from 3 heats. The reduced technical risk enabled by the AIM methodology accelerates technology insertion through substantial reduction of certification investment risk.

Summary: As the first prospective application of the Accelerated Insertion of Materials (AIM) technology, QuesTek is demonstrating the validation and adoption of Ferrium S53 for structural applications in landing gear, under an Environmental Security Technology Certification Program. The goal of the AIM application to Ferrium S53 is to produce a mechanistic-based method for estimating the static mechanical property variation with calibration by minimal data sets. The Metallic Materials Properties Development and Standardization handbook ultimately requires 100 measurements of yield and ultimate tensile strength data from 10 heats of material to establish A-basis minimum design properties. The AIM procedure on Ferrium S53 estimates the A-allowable tensile strength values from science-based modeling using only an S-basis data set of 30 measurements collected from 3 heats. In order to increase the prediction confidence with this limited data, the method integrates traditional Bayesian analyses and model synthesis with the data. This fusion of science based modeling with data greatly improves estimation and prediction, which reduces the uncertainty. The reduced technical risk enabled by the AIM methodology accelerates technology insertion through substantial reduction of certification investment risk.