Manufacturing & Assembly Process Simulation

Sequential Assembly Simulation

Multi-step assembly sequence idealization to determine residual stresses as a function of the fastening sequence.

Virtual Manufacturing & Integrity Assessment

Simulating the physics of forming to predict post-process defects

Post-Forming Springback & Compensation

Non-linear analysis of geometric deviations after forming and the optimization of tool geometry for compensation.

Surrogate Modeling & ROMs

Machine Learning driven development of Reduced Order Models (ROMs) and surrogate solvers that provide near-instantaneous structural predictions for high-dimensional design spaces.

ML-Augmented Model Refinement & Discrepancy Modeling

Implementing ML-driven error-correction loops that utilize physical test results to train and refine simulation parameters, effectively bridging the gap between idealized models and real-world structural behavior.

Physics-Informed Neural Networks (PINNs)

Integrating traditional FEA data with Physics-Informed Machine Learning to enhance the predictive accuracy of complex non-linear behaviors and material failure.

Automated Simulation Orchestration

Implementing AI-driven algorithms for automated geometry cleanup, high-fidelity meshing, and boundary condition setup to drastically reduce simulation lead times.

Predictive Design Space Exploration

Leveraging ML-based sensitivity analysis to rapidly identify optimal architectural configurations before committing to expensive high-fidelity solver iterations.