Key takeaways
- 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.