This study presents an automated material calibration framework for finite element analysis (FEA) of pharmaceutical tablet compaction, enabling direct translation of experimental data into validated Ansys material models to derisk tablet quality failures. The system employs a closed-loop workflow where compaction profiles (axial force, displacement, and density measurements) are processed through constrained optimization algorithms to calibrate powder constitutive model parameters (e.g., Drucker-Prager cohesion, hardening exponents) without manual intervention. Machine learning surrogate models, trained on a diverse dataset of pharmaceutical blends, predict critical quality attributes (CQAs) such as tensile strength and porosity from FEA-simulated stress distributions. By automating the linkage between compaction data and nonlinear FEA, the framework identifies high-risk failure modes—capping, lamination, and friability hotspots—with 85–90% correlation to physical dissolution and crush testing outcomes.
The framework’s Ansys integration exports ANSYS Mechanical APDL scripts with automated material card generation, enabling rapid "what-if" scenarios for formulation changes. In a case study with brittle APIs, the system reduced late-stage tablet failures by 50% by preemptively flagging stress-assisted crystallization risks during virtual prototyping. This approach provides pharmaceutical developers a physics-informed roadmap to tablet quality assurance, prioritizing critical experiments while maintaining alignment with ICH Q8 design space principles.
