Neural Print Time Estimation for Production Printing Variability

The patent replaces the traditional formula-based mechanical estimation system with a neural network model that learns from actual production data. The neural network substitutes the deterministic calculation approach with a data-driven predictive model that captures real-world variations in printing performance, thereby improving estimation accuracy while accepting increased system complexity.

The system implements feedback by continuously collecting actual print time data from sensors during printing operations and using this data to train and refine the neural network model. This feedback loop allows the model to adapt to changing conditions and improve its predictions over time, resolving the contradiction between accuracy and complexity through iterative learning.

The system performs preliminary action by collecting and storing production data during normal printing operations before estimation is needed. Sensors continuously capture parameters such as print speed, paper properties, and environmental conditions, building a comprehensive dataset that the neural network can later use to provide accurate real-time estimates without adding delay to the printing process.

The system uses self-service by automatically collecting production data through integrated sensors and using this data to train the neural network model without requiring external intervention. The printing system itself generates the training data through its normal operations, eliminating the need for separate data collection phases and reducing the time loss associated with manual data gathering.