Adhesive Dispenser Calibration With ML for Precise Flow Control

The patent replaces expensive, complex neural network models with simpler, more economical machine learning algorithms that require minimal calibration data. This principle is applied by using lightweight algorithms that can be quickly trained and discarded or updated without requiring extensive calibration resources, thereby reducing both time and data requirements while maintaining adequate prediction accuracy for adhesive dispensing applications.

The patent changes the parameters of the machine learning approach by using algorithms with fewer parameters and lower computational requirements compared to neural networks. This allows the system to achieve sufficient prediction accuracy with significantly reduced calibration data, transforming the problem from one requiring hundreds of calibration points to one that can be solved with minimal calibration while maintaining operational effectiveness.

The patent replaces complex active control systems with a predictive modeling approach that uses machine learning algorithms to calculate optimal dispenser settings. Instead of using sophisticated sensors and actuators for real-time flow rate control, the system predicts the required settings based on adhesive properties and environmental conditions, thereby achieving accurate flow control through computational methods rather than mechanical control systems.

The patent applies preliminary action by pre-calculating optimal dispenser settings using machine learning models before actual dispensing occurs. The system predicts the appropriate pressure, temperature, and other parameters based on adhesive characteristics and environmental factors, allowing the dispenser to be configured in advance rather than requiring complex real-time active control during the dispensing process.