A second major innovation is the integration of artificial intelligence with adaptive process control to create self-optimising manufacturing systems. Instead of using AI solely for inspection, my research allows AI predictions to directly influence machine behaviour through an intelligent feedback loop.

Using a YOLO26 machine vision system connected to an Arduino-controlled actuator, the milling machine continuously analyses grain quality and automatically adjusts motor speed and vibration in real time whenever excessive grain breakage is detected. This transforms quality inspection from a passive monitoring process into an active optimisation system capable of correcting production conditions as they occur.

This AI-driven closed-loop architecture represents an important step toward Industry 4.0 by combining computer vision, cloud computing, embedded systems, and intelligent control into a unified autonomous manufacturing platform that improves efficiency, reduces waste, and enhances product consistency.