Cutting Edge Heat Map Analysis for Parameter Influence Detection

The patent replaces the mechanical trial-and-error testing system with an artificial intelligence-based image analysis system. The neural network analyzes images of cut edges and automatically determines the influence of cutting parameters, substituting extensive physical test series with computational analysis that provides rapid results without time-consuming material testing.

The patent creates a virtual model of the cutting process through image analysis and neural network training. Instead of repeatedly performing physical cut tests, the system uses images (copies) of cut edges to train and validate the neural network, which then predicts parameter influences without requiring additional physical testing.

The patent transforms the analysis from simple image processing to advanced neural network-based parameter extraction. By changing the computational parameters and algorithms used (from traditional image analysis to deep learning with backpropagation), the system achieves significantly higher precision in determining cutting parameter influences while maintaining automated operation.

The patent introduces a neural network as an intermediary between the cut edge image and the cutting parameter determination. This intermediary layer processes the image data through multiple computational stages, extracting features and relationships that direct analysis methods cannot detect, thereby improving measurement precision without requiring complex manual intervention.

The patent segments the neural network into distinct functional modules: image preprocessing, feature extraction layers, backpropagation computation, and parameter determination output. This segmentation allows each module to be optimized independently and facilitates implementation using standard deep learning frameworks, managing complexity through modular architecture.

The patent creates a universal neural network architecture that can determine multiple cutting parameters simultaneously from a single image analysis. The system is designed to handle various parameter types (power, speed, gas flow, etc.) through a unified computational framework, reducing overall system complexity compared to having separate analysis systems for each parameter.