+128% more productivity in laser metal deposition
How aiXopt increases productivity in a high-variance process while meeting quality specifications.
10 safe experiments to the optimal parametrization
How aiXopt optimally tunes a process engineering system with multiple components, operating points and safety limits without putting the plant at risk.
- Research cooperation
- Approach transfers to process engineering systems with several coupled components, changing operating points and hard safety limits.
Challenge
A coupled tank system has to control different fill levels while critical process limits must be respected at all times. Pump, valves and controller interact, the right settings depend on the operating point, and a setting that is too aggressive pushes the plant to its limit. Mastering this by hand takes a lot of experience and many trials.
Solution
aiXopt's Small-Data AI learns directly on the plant which settings respect the critical process limits with high probability. If a run approaches a limit, the plant's safe fallback strategy takes over, and aiXopt uses exactly that run as information about where the limit lies. Every experiment stays safe, and after around ten experiments the optimal parametrization is found, staying within the limits in every validation run. Across the whole operating range this yields a characteristic curve that adapts the settings automatically to the current operating point.
More references
−66% fewer production defects in cold rolling
How aiXopt lowers the defect rate in a high-volume process.
Faster setup of new yarn types in ring spinning
How aiXopt tunes a process using nothing but qualitative feedback from the operators.
100% robust controller settings in milling
How aiXopt tunes the control loops of an existing milling machine to varying machine behavior and gets the most out of the hardware already in place.
94% tolerance compliance in temperature control
How aiXopt tunes a multi-zone temperature control across the whole operating range instead of for a single operating point.
3 objectives instead of 5 abstract controller gains
How aiXopt makes a path controller tunable through understandable objectives instead of controller parameters only experts understand.
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