Acoustic Monitoring for Performance and Reliability in Metal Production

In metalworking environments, whether it’s extrusion, pressing, CNC machining, or general fabrication, equipment must operate with a high degree of precision under considerable mechanical stress. Reliability, consistency, and safety are not just ideals, but everyday requirements.

From On-Site Observation to Remote Operation

As the industry moves toward automation and remote operation, production floors are seeing fewer boots on the ground. Machines are increasingly overseen from control rooms, guided by data rather than direct human observation. While this transition brings greater efficiency and reduced manual workload, it also introduces a critical gap: the absence of human senses, like the sense of hearing, on the factory floor.

Hearing What Machines Can't Show You Visually

Some faults in industrial machinery don’t show up on dashboards or visual inspections—they are heard. Subtle changes in sound can be the first sign of a problem. Squarehead’s directional microphone arrays, equipped with beamforming technology, offer an alternative to traditional monitoring. These systems isolate and analyse the acoustic signatures of potential faults—capturing what the human ear might miss, especially in noisy, high-intensity environments.

Whether it’s the early signs of tool wear in CNC operations, misalignment in presses, or lubrication issues in extrusion systems, these deviations often announce themselves acoustically before becoming measurable by other means. The system recognises these anomalies in real time, providing insight when and where it matters.

Enabling Predictive Maintenance Through Sound

Processes like extrusion and rolling are repetitive by nature, and this repetition extends to the acoustic footprint of certain defects. For example, blisters in aluminium extrusion—a result of trapped gases escaping during forming—produces a distinct popping sound. In environments where heat, noise, and dust make traditional sensing methods unreliable, these sounds offer a reliable indicator.

By applying machine learning to live acoustic data, Squarehead’s systems can identify these fault signatures as they occur. Pre-trained models classify deviations based on sound frequency, duration, and intensity, allowing maintenance teams to act pre-emptively. The result: less downtime, more stable operations, and higher product quality.