Inversion of tantalum micromechanical powder consolidation and sintering models using bayesian inference and genetic algorithms

A Bayesian enhanced genetic algorithm (GA) addresses the inverse and ill-posed problem of optimizing the 19 parameters of micromechanical powder densification models for tantalum using limited and uncertain data sets that leave the optimization problem underdetermined. Additionally, the posterior probability density evolved by the GA provides a parameter sensitivity analysis as well as a guide to experimental design which significantly assists in the development of accurate models with a minimum of experimentation.