Real-Time Surface Roughness Analysis in Milling Using Acoustic Emission Signals for Industry 4.0 Applications

In the expansion of Industry 4.0, many automation processes are being enhanced as a means of accomplishing higher productivity goals.With the prospect of new achievable objectives, the demand for faster and more reliable resource processing methods has also increased.Similarly, machining processes have also been improved with the development of IoT devices by streamlining operations, enabling predictive maintenance, and providing real-time data for better decision-making and collaborating with such productivity levels.For instance, in metal milling, IoT-based sensor techniques are being developed and proven efficient in increasing speed and reliability while reducing system invasiveness and complexity, which grants more profitability.The present paper proposes a real-time metal roughness average (Ra) analysis method based on Acoustic Emission (AE), which indirectly estimates roughness through signal processing and feature extraction of the AE signal through Power Spectral Density (PSD) evaluation.The experimental setting consists of a steel workpiece in which straight lines were milled with four distinct roughness levels (6 m, 12 m, 18 m, and 24 m, produced by defined milling parameters), and the method was able to estimate the Ra with an error under 7%.This work aims to contribute to the real-time monitoring of surface roughness in alignment with Industry 4.0 requirements by demonstrating the effectiveness of IoT-based solutions and the potential of Acoustic Emission in machinery sensing and process automation.