Enhanced neurosymbolic architecture for computer numerical control operations

A computing system for controlling computer numerical control (CNC) operations employing a neurosymbolic platform, the computing system comprising:

• one or more hardware processors configured for:
• collecting multimodal data about a manufacturing process through a plurality of sensors, comprising at least visual, force, and thermal data;
• processing the multimodal data using machine learning models to detect current process states, identify quality variations, and predict process outcomes;
• maintaining a knowledge graph comprising manufacturing states, operational constraints, and process relationships;
• generating real-time control decisions by combining machine learning outputs with knowledge graph constraints, optimizing process parameters based on combined analysis, and adapting control strategies to changing conditions;
• implementing manufacturing control by translating control decisions into machine-specific commands, coordinating execution across manufacturing equipment, and monitoring operational outcomes;
• and
• continuously refining manufacturing knowledge by analyzing operational data and outcomes, updating process models and constraints, and optimizing control strategies based on performance.