Geometry-Aware Defect Region Prediction in Manufacturing

The patent merges product geometry data with process parameters by integrating CAD model information (volumetric data, surface area, wall thickness, cooling channel geometry) into the AI model input layer. This combination allows the neural network to simultaneously process both geometric and process information, enabling accurate defect prediction that accounts for the interaction between product design and manufacturing parameters.

The patent transitions from traditional 2D process parameter space to a multi-dimensional input space that includes 3D geometric features. By incorporating volumetric measurements, surface area ratios, and spatial distribution of cooling channels, the system adds geometric dimensions to the prediction model, enabling comprehensive defect analysis that considers both process and design factors.

The patent replaces complex physics-based simulation systems with an artificial neural network model. The AI model learns optimal process parameters and defect predictions from training data without requiring explicit physics equations or boundary condition specifications. This substitution maintains manufacturing precision while dramatically reducing system complexity and computational requirements.

The patent transforms the simulation approach from solving complex differential equations with multiple boundary conditions to using a trained neural network that directly maps input parameters to output predictions. By changing the computational paradigm from physics-based calculation to data-driven prediction, the system achieves similar accuracy with significantly reduced complexity.