Bayesian Optimization of Process Parameters of a Sensor-Based Sorting System using Gaussian Processes as Surrogate Models
This work addresses the need for efficient parameter tuning in industrial sorting systems, though it appears incremental as it applies existing Bayesian optimization methods to this domain.
The paper tackles the problem of optimizing process parameters for sensor-based sorting systems, which require continuous adjustment due to changing material streams, by introducing a Bayesian optimization approach using Gaussian process regression as surrogate models to minimize experiments and handle uncertainties, achieving specific requirements for system behavior.
Sensor-based sorting systems enable the physical separation of a material stream into two fractions. The sorting decision is based on the image data evaluation of the sensors used and is carried out using actuators. Various process parameters must be set depending on the properties of the material stream, the dimensioning of the system, and the required sorting accuracy. However, continuous verification and re-adjustment are necessary due to changing requirements and material stream compositions. In this paper, we introduce an approach for optimizing, recurrently monitoring and adjusting the process parameters of a sensor-based sorting system. Based on Bayesian Optimization, Gaussian process regression models are used as surrogate models to achieve specific requirements for system behavior with the uncertainties contained therein. This method minimizes the number of necessary experiments while simultaneously considering two possible optimization targets based on the requirements for both material output streams. In addition, uncertainties are considered during determining sorting accuracies in the model calculation. We evaluated the method with three example process parameters.