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Robust optimization of deformation processes for control of microstructure-sensitive properties Cornell University, Nicholas Zabaras. 60. Mean. std. ±. 50. 40. Equivalent stress (MPa). 30. 20. 10. 0. 0. 1. 2. 3. Equivalent strain. -4. x 10.
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Robust optimization of deformation processes for control of microstructure-sensitive properties Cornell University, Nicholas Zabaras 60 Mean std ± 50 40 Equivalent stress (MPa) 30 20 10 0 0 1 2 3 Equivalent strain -4 x 10 • Long-Term PAYOFF: Decrease in processing costs and improvement of material properties in metal forming. • OBJECTIVES • Develop mathematical techniques for optimization of metal forming processes in the presence of process and material uncertainty • Develop multi-scale optimization techniques for controlling microstructure-sensitive properties during forming processes. Design of a steering rod Property variability induced by topological uncertainty in microstructures • APPROACH/TECHNICAL CHALLENGES • Continuum stochastic sensitivity analysis for robust optimization of metal forming processes • Finite element modeling using spectral and support space representation of material uncertainties • Multi-scale linking and sensitivity analysis for designing materials with improved properties • ACCOMPLISHMENTS/RESULTS • Developed efficient stochastic deformation processing codes • Novel multi-scale process optimization codes developed for controlling material properties • FUNDING ($K)—Show all funding contributing to this project • FY05FY06FY07FY08FY09 • AFOSR Funds 150K 150K 150K • AFOSR/DURIP 150K • TRANSITIONS • Numerous journal publications can be found in http://mpdc.mae.cornell.edu/ • STUDENTS, POST-DOCS • B Velamur Asokan, S Acharjee, V Sundararaghavan, S Sankaran • LABORATORY POINT OF CONTACT • Dr. Dutton Rollie, AFRL/MLLMP, WPAFB, OH
MULTISCALE CONTROL OF METAL FORMING PROCESSESDesign of materials with tailored properties Closed die forging • Design of optimal preform shapes for obtaining uniform strength properties • Objectives: Minimize variations in material strength, achieve desired shape • Problem size: ~1 billion micro + macro-scale degrees of freedom • Methodology: Multi-scale sensitivity analysis • Objective reached in 8 design iterations Macro-scale Optimize macro-scale properties that are non-linear functions of the microstructure Linking Micro-scale Yield strength (MPa) Optimized product with uniform strength Unoptimized Polycrystalline texture in Rodrigues-Frank space N. Zabaras, Cornell University