The research objective of this study is to identify the slip system parameters of Ti-6Al-4V, an aerospace alloy with a wide range of applications in jet engine components, under the effects of process-related uncertainty using novel surrogate optimization methods. In particular, the surrogate optimization is applied to calibrate the initial slip resistance, hardening modulus, power law exponent, and saturation stress at room temperature, using experimental stress-strain data.
The surrogate optimization scheme utilizes Radial Basis functions to minimize the errors between predicted and experimental stress-strain curve parameters, such as Young’s Modulus, yield strength, and four slopes extracted from the plastic region. The effects of process-related uncertainty on experimental data are also considered when performing the optimization. We show that the surrogate optimization algorithm iteratively improves the computational predictions for alpha- and beta-phase parameters while using a cost-efficient online training strategy, thereby leading to an optimum solution with low computational cost.