AI-Assisted CAD Validation

A Tier 1 automotive supplier producing plastic interior modules and structural parts faced mounting pressure from OEM clients to shorten design cycles without compromising safety validation. Traditional CAD/CAE workflows for crashworthiness, airflow, and thermal simulations required long solver runtimes and manual setup, delaying design sign-off by several weeks. The client engaged Sphere to run a Proof of Concept that applied AI-assisted simulation acceleration to reduce validation timelines while maintaining engineering accuracy.

Sphere delivered a focused PoC that integrated AI-assisted solvers and surrogate modeling into the client’s existing CAD/CAE workflow:

Data Preparation

Surrogate Modeling

Hybrid Workflow

Integration & Validation

• Collected 3 years of historical simulation runs (crash, airflow, and thermal) stored in ANSYS and CATIA.

• Standardized geometry inputs, meshing parameters, and output metrics (stress, deformation, airflow rates).

• Trained neural network surrogate models on historical simulation results to predict outputs for new design variations.

• Applied regression-based feature selection to identify which geometry parameters most influenced results.

• AI models provided quick preliminary predictions for new designs (minutes instead of days).

• High-confidence predictions were passed directly to engineering review, while borderline cases were still run through full solvers.

• Engineers could test 3–4x more design iterations before committing to a final simulation run.

• Built a lightweight API layer to connect ANSYS/CAE software with the surrogate prediction engine.

• Conducted side-by-side validation runs: AI-predicted vs. solver-computed results.

• Accuracy reached >92% correlation on deformation and airflow metrics for standard parts.

The PoC is currently enabling engineers to test more design alternatives per cycle, giving greater flexibility in early-stage innovation. OEM-required accuracy levels are being maintained through a hybrid validation approach (AI-assisted predictions combined with solver verification). A repeatable pipeline for AI-assisted design validation has been established, and the team is now exploring targeted extensions into related workflows such as thermal management studies and lightweight component optimization, where faster iteration provides clear value without compromising safety checks.