Sensor Image Recognition for Laser Cutting Quality Analysis

The patent replaces conventional analytical and mechanical description methods with a data-driven neural network model. The system uses sensors to collect process data and feeds it to a neural network that learns the complex relationships between process parameters and quality outcomes, substituting traditional analytical approaches with an intelligent computational model that can capture non-linear process behavior without requiring explicit analytical formulations.

The patent transforms the approach to process description by changing from fixed analytical parameters to dynamic data-driven parameters. The neural network model processes sensor data in real-time, adapting its internal parameters (weights and biases) during training to accurately represent the complex process relationships. This allows the system to capture evolving process characteristics without being constrained by predetermined analytical models.

The patent implements preliminary action by training the neural network model offline using historical process data before real-time monitoring begins. During the training phase, the model learns the complex relationships between process parameters and quality outcomes from labeled datasets. Once trained, the model is deployed for real-time inference, where it can rapidly predict quality metrics without requiring complex calculations during actual processing, thus minimizing data processing time while maintaining high reliability.

The patent creates a virtual copy of the laser processing system through the neural network model. This digital twin or virtual model replicates the behavior of the physical system by learning from sensor data, allowing quality assessment and prediction without requiring direct real-time analysis of all raw sensor signals. The model copies the essential process dynamics and uses them for rapid quality evaluation.