AI design validation · Human in the loop
Let AI dream the part. Let ETS make sure it can be built.
Simulation solvers and generative tools now return an answer in seconds. What they return is a high-speed compass, not the map: they don’t know your press, your resin lot, your supplier or the ten thousand ways parts have failed in production. ETS is the validation layer between the model and the steel.
The AI knowledge gap
Fast answers, missing context.
AI is making engineers faster at generating designs, and creating a dangerous illusion of competence along the way. Three gaps show up again and again.
Simulation isn’t reality
Real-world processing quirks, machine behavior and resin variation live outside the model.
Tribal knowledge is missing
DFM judgment lives in senior engineers’ heads, not in training data. AI can’t replicate four decades of failure outcomes.
Edge cases are invisible
Machine-specific constraints, batch variation and supplier capability rarely make it into a generative design brief.
How we validate
Complementary to AI. Not a replacement.
Keep the speed. Add the judgment.
Real-world cases
Actual failures and successes: the training data AI lacks.
Edge-case detection
Ejector issues, weld-line placement, cooling imbalance: what solvers miss.
Material-specific judgment
How a specific resin behaves on a specific machine.
Production translation
What the simulation result actually means for your production line.
White paper
What AI simulation can’t tell you.
The failure modes behind real DFM, and where AI-only validation breaks down.
Next step
Before the AI design becomes a steel design.
Send the model and the simulation results. A senior engineer scopes the validation within one business day.
- NDA on request
- Senior engineer replies in one business day
- Fixed price where applicable
- Your data stays yours