Driven by the growth of Industry 4.0, advanced studies on machine integration and manufacturing automation through IoT systems are progressing rapidly. Specifically, 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. Traditionally, creating acoustic maps that visualize the process and correlate with machining conditions requires an external synchronization signal, usually provided by an encoder attached to the spindle. This study introduces an innovative technique 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. Implemented in Matlab software, the algorithm uses digital filters to extract features, recognizing the pattern of the cutting edges in the map. Based on the misalignment of the cutting edges in the image, the algorithm automatically adjusts the rotation parameters in the map reconstruction, resulting in an accurate representation of the process. The results demonstrate that under specific machining conditions, the need for an external synchronization signal to construct an acoustic map is unnecessary, making the data acquisition system simpler, more economical, and computationally less demanding. This advancement significantly contributes to the development of embedded IoT sensor solutions tailored for Industry 4.0.