Self-Learning Machining Simulation for Digital Machine Model Adaptation

The patent creates a digital twin (virtual copy) of the real machine tool system that replicates its behavior and state parameters. This digital model is continuously updated through comparison with actual sensor data from the physical machine, enabling accurate simulation without requiring complex manual specification of all state parameters. The copying approach allows the virtual system to inherit the complexity of the real system while enabling precise parameter specification through automated data transfer.

The patent implements a feedback mechanism where simulation results are continuously compared with actual machine tool data from sensors. Discrepancies are used to update and refine the digital model parameters, improving accuracy over time. This closed-loop feedback system automatically adjusts the virtual model to match the real system's behavior, resolving the contradiction between accuracy and complexity by using iterative refinement rather than static complex modeling.

The patent performs preliminary adaptation of the digital machine model by automatically importing manufacturer data and initial parameter sets before actual use. This preliminary configuration reduces setup time while maintaining accuracy, as the model is pre-adjusted to match the specific machine tool being simulated. The system prepares the digital twin in advance with default parameters that can be quickly refined using initial sensor data, avoiding time-consuming manual adaptation during commissioning.

The system performs self-adaptation by automatically comparing its simulation outputs with sensor data from the real machine tool and adjusting its own parameters without external intervention. This self-service capability eliminates the need for manual setup and adaptation efforts, allowing the digital model to automatically refine its accuracy over time as it operates, thus improving simulation accuracy without increasing setup time.

The patent replaces manual mechanical adjustment and specification of machine parameters with automated electronic data processing. Sensor data from the real machine tool is automatically captured, processed, and used to update the digital model parameters. This substitution of manual processes with automated electronic systems maintains high simulation precision while dramatically reducing adaptation effort, as the system self-updates its parameters without requiring manual intervention for each parameter adjustment.

The patent creates a universal digital model framework that can represent multiple machine tool types and configurations using a standardized set of parameters and data structures. This universal approach allows the same simulation system to adapt to different machines without requiring custom development for each case, reducing adaptation effort while maintaining precision through consistent parameter specification methods across diverse machine tools.

The system performs self-optimization of simulation parameters by automatically comparing its outputs with sensor data from the real machine tool and adjusting its own parameters without human intervention. This self-service capability achieves high parameter accuracy while maintaining full automation, as the system learns and adapts its parameters autonomously through iterative comparison and adjustment based on actual machine behavior data.

The patent implements automated feedback loops where simulation results are continuously compared with actual machine data, and parameter adjustments are automatically made based on the discrepancies. This closed-loop feedback system eliminates manual optimization while maintaining high accuracy, as the automated comparison and adjustment process continuously refines parameters to match actual machine behavior, removing human error while preserving precision.