An AI-based system using acoustic pattern recognition has been developed to enable Computer Numerical Control (CNC) machine to possess self-diagnosis and provide feedback for machine operation. CNC machines are widely used in the manufacturing sector and they are machine tools that cut or move material as programmed on the numerical controller. Sounds associated with various cutting conditions such as normal cutting, heavy cutting, tool wear, and tool crash, can be recognised by the system, and this information can then be feedback to the actuators or end users for necessary actions. The system would facilitate remote monitoring of the machines, predictive maintenance, and failure analysis of the machine tools. System could be further adapted or enhanced to work for different types of instruments and acoustic sounds.
The system developed made use of edge AI and software platform for smart manufacturing to enable faster and better decision making. Acoustic data source could be collected conveniently to monitor the real-time machine operation status so that timely actions can be taken when necessary. The system requires minimum interference with the operation and setup of the machines and can be integrated with a wide variety of instruments. The system also offers flexibility to have its AI trained to recognise other types of sounds to address different application needs.