+128% more productivity in laser metal deposition
How aiXopt increases productivity in a high-variance process while meeting quality specifications.
Faster setup of new yarn types in ring spinning
How aiXopt tunes a process using nothing but qualitative feedback from the operators.
- Research cooperation
- Approach is especially useful for processes where quality is judged subjectively and measurements are tedious and expensive.
Challenge
In ring spinning, the machine settings have to be found anew for every new yarn type. Yarn quality is mostly judged subjectively, and lab measurements are tedious and expensive. That made setting up the process slow.
Solution
aiXopt's Small-Data AI works even without quantitative measurements: it learns from the operators' qualitative feedback alone, whether quality got better or worse, and derives the right process parameters from that. The effort for lab measurements drops significantly and setup gets around 50% faster.
More references
−66% fewer production defects in cold rolling
How aiXopt lowers the defect rate in a high-volume process.
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.
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.
Your machines can do more.
Let's find out how much.
Book a no-obligation call and find out where your biggest potential lies.