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 μm, 12 μm, 18 μm, and 24 μm, 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.

This work presents a system combining hardware and embedded software to simplify the acquisition of acoustic emission signals using a wireless IoT sensor. Integrated into a larger ecosystem, this system supports fault diagnosis, feature extraction, pattern classification, and a cloud interface. It consolidates complex apparatus into a single tool, enabling remote sensor configuration during tests. The system also incorporates computational models for feature extraction and failure analysis, organizing tests through forms without needing external computers. This innovation advances the use of acoustic emission sensors in line with Industry 4.0, enhancing IoT sensor applications and improving manufacturing process efficiency.

In milling applications of CFRP (Carbon Fiber Reinforced Polymer) composites, acoustic emission sensors employing piezoelectric transducers have been used to generate acoustic maps. These maps are crucial for monitoring the condition of both the tool and the workpiece, providing a visual analysis of the tool-workpiece interaction that facilitates decision-making by the operator in case of failures. This study introduces a technique-implemented in the Matlab software-that uses the image generated by the acoustic map to perform automatic alignment during the map's production, eliminating the need for an external synchronization signal.