- H2O Driverless AI can be used to build models that predict test outcomes, such as strength, fit, or yield, from your own design and test history.
- Engineers can rank candidate configurations by their predicted chance of passing and spend prototype and destructive-test budget on the best ones.
- Explanations show which materials, dimensions, and process settings drive each prediction, so engineers can check the model against what they know.
- Every new test result becomes training data, so the model improves with each design cycle.
Every design has to be proven the expensive way
Proving that an assembly is safe and cost-effective often takes rounds of prototypes and destructive tests. Each round consumes material, lab time, and weeks of your engineers' schedule.
Your teams already hold the evidence to narrow the search: material properties, past configurations, test reports, and field returns. It sits in different systems, and nobody has time to join it before the next design review.
You are measured on time to market, quality, and cost. Testing every plausible combination protects quality, but it burns the other two.
How predictive manufacturing design works
H2O Driverless AI turns your design and test history into models that predict outcomes for new candidates, so testing confirms good designs instead of searching for them.
Step 1: join the history
Combine material properties, dimensions, process parameters, and supplier data with the test results and field performance of past designs.
Step 2: build the model
H2O Driverless AI trains regression models for continuous outcomes such as strength or yield, and classification models for pass or fail.
Step 3: find the signal
Automatic feature engineering tests interactions between variables that are hard to see by hand, such as a material grade that only fails at a certain thickness.
Step 4: rank the candidates
Score the configurations your engineers are considering, and send the ones with the best predicted outcome to prototype and test first.
Step 5: explain and learn
Shapley values show which variables drive each prediction, so engineers can check the model against what they know. Each new test result goes back into the training data.
Where testing still matters
The model does not replace physical testing; it changes what gets tested first. Every design is still confirmed by a physical test, and each result improves the model for the next design cycle.