AI-Assisted CAD Validation

Learn how a Tier 1 automotive supplier used AI-assisted CAD/CAE validation and surrogate modeling to cut simulation time, accelerate design cycles, and meet OEM program deadlines.

CLIENT

Tier 1 automotive supplier (plastic modules & structural parts)

INDUSTRY

Automotive Manufacturing & Suppliers

SERVICE

AI-assisted CAD/CAE validation | Surrogate modeling | Simulation acceleration | Hybrid validation pipeline | Data preparation & standardization | OEM program enablement

Overview

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.

Challenges

The client’s engineering teams were constrained by traditional CAD/CAE processes that could not keep pace with OEM demands for faster vehicle program delivery. Simulation runtimes were long, setup was manual, and only a limited number of design alternatives could be validated. As a result, design flaws often surfaced late, forcing rework and putting the supplier at risk of missing critical milestones.

Long Simulation Runtimes

Crash and airflow simulations in ANSYS and CATIA took days to complete, slowing design iteration and delaying OEM program milestones.

Manual Setup & Processing

Engineers spent significant time preparing meshing, setting boundary conditions, and cleaning outputs before results could be reviewed.

Late Detection of Design Flaws 

Because of long runtimes, only a limited number of design variations were tested. Issues often surfaced late, forcing costly rework.

OEM Pressure on Timelines 

OEM clients demanded faster validation to align with accelerated EV program launches, putting the supplier at risk of losing future contracts.

Our Solution

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

Data Preparation

  • 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).

Surrogate Modeling

  • 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.

Hybrid Workflow

  • 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.

Integration & Validation

  • 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.

Key Achievements

Reduced average simulation setup + runtime from 2–3 days to under 12 hours.

30–40% reduction in validation timelines, accelerating OEM program milestones.

2–3 weeks saved in overall vehicle module development cycles.

Higher OEM satisfaction, with the supplier demonstrating faster responsiveness and design agility.

Result

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.

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Luke Suneja

Client Partner

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