An AI-Driven Framework for Real-Time Surface Roughness Prediction and Anomaly Detection in CNC Machining using Multi-Sensor Fusion.

This report outlines a conceptual framework for integrating Artificial Intelligence (AI) and multi-sensor fusion to enable real-time surface roughness prediction and anomaly detection in Computer Numerical Control (CNC) machining. The proposed framework overcomes the critical limitations of traditional, post-process quality control methods by leveraging continuous data streams from multiple sensors. By fusing data on cutting forces, vibrations, temperatures, and other parameters, the system employs advanced AI models—such as hybrid Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU) networks for prediction and autoencoders for anomaly detection—to provide immediate, actionable insights. This paradigm shift from reactive to proactive quality management promises to enhance product quality, reduce waste, increase operational efficiency, and pave the way for fully autonomous and adaptive manufacturing processes.