Reduced Order Numerical Modeling for Calibration of Complex Constitutive Models in Powder Pressing Simulations

Abstract

Numerical simulations of different ceramic production phases often involve complex constitutive models, with difficult calibration process, relying on a large number of experiments. Methodological developments, proposed in present paper regarding this calibration problem can be outlined as follows: assessment of constitutive parameters is performed through inverse analysis procedure, centered on minimization of discrepancy function which quantifies the difference between measurable quantities and their computed counterpart. Resulting minimization problem is solved through genetic algorithms, while the computational burden is made consistent with constraints of routine industrial applications by exploiting Reduced Order Model (ROM) based on proper orthogonal decomposition. Throughout minimization, a gradual enrichment of designed ROM is used, by including additional simulations. Such strategy turned out to be beneficial when applied to models with a large number of parameters. Developed procedure seems to be effective when dealing with complex constitutive models, that can give rise to non-continuous discrepancy function due to the numerical instabilities. Proposed approach is tested and experimentally validated on the calibration of modified Drucker-Prager CAP model, frequently adopted for ceramic powder pressing simulations. Assessed values are compared with those obtained by traditional, time-consuming tests, performed on pressed green bodies.