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Meeting	MS&T24: Materials Science & Technology
Symposium	Uncertainty Quantification Applications in Materials and Engineering
Presentation Title	Bayesian Protocols for High-Throughput Optimization of Kinematic Hardening Models Using Cyclic Microindentation Experiments
Author(s)	Aditya Venkatraman, David McDowell, Surya Kalidindi
On-Site Speaker (Planned)	Aditya Venkatraman
Abstract Scope	We develop Bayesian protocols to iteratively refine both model forms and material property calibrations. Our aim is to provide rigorous, probabilistic evaluations of advancements achieved with increasing model complexity. Utilizing experimental microindentation data, the protocols involve three steps: emulating FE simulations using multi-output Gaussian process surrogate models, calibrating an initial simple constitutive model against experimental data, and progressively enhancing model complexity by iteratively improving agreement between simulations and experiments. The various model forms are compared using model form probabilities and aggregate discrepancies. Sobol indices are used to quantify the identifiability of material properties, aiming to prevent parameter proliferation. We apply this protocol to identify the optimal form of cyclic plasticity models for duplex Ti-6Al-4V. Although tailored for cyclic plasticity models, these protocols hold promise for refining nonlinear, path-dependent physics-based models across diverse material classes.

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