+128% more productivity in laser metal deposition
How aiXopt increases productivity in a high-variance process while meeting quality specifications.
How aiXopt makes a path controller tunable through understandable objectives instead of controller parameters only experts understand.
The path controller of an automated system has 5 weighting parameters whose effect on the behavior is hard to predict even for experts. What is actually required are 3 tangible things: path accuracy, speed and smoothness. They conflict, and depending on the situation a different compromise is right. In addition, some settings make the system leave the path, and the trial has to be aborted.
aiXopt's Small-Data AI does not search for a single setting but for the set of all optimal trade-offs between the 3 objectives, using aborted trials as information along the way. The result is a set of validated settings in which a user moves along the objectives, for example more smoothness instead of speed, without having to understand the controller parameters themselves. With a small trial budget the approach clearly outperformed all compared methods.
How aiXopt increases productivity in a high-variance process while meeting quality specifications.
How aiXopt lowers the defect rate in a high-volume process.
How aiXopt tunes a process using nothing but qualitative feedback from the operators.
How aiXopt optimally tunes a process engineering system with multiple components, operating points and safety limits without putting the plant at risk.
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.
How aiXopt tunes a multi-zone temperature control across the whole operating range instead of for a single operating point.
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