Multi-sensor and MTConnect dataset of metal cutting anomaly in milling from laboratory and industry settings.

This paper presents the Multi-Sensor and MTConnect (MSM) dataset, an open-access resource for anomaly detection in computer numerical control (CNC) metal milling. The dataset integrates synchronized signals from sound sensors, accelerometers, current transformers, and MTConnect-based machine controller data, collected from both laboratory experiments and real industrial production. It covers diverse machining conditions, including normal operations, process anomalies, and tool defects. All data were reviewed and annotated by domain experts using a three-level scheme, enabling consistent labeling across machines and environments. The dataset spans multiple CNC mills, cutting tools, workpiece materials, and cutting conditions, and includes MTConnect information models to ensure semantic consistency and reproducibility. Each dataset unit is time-aligned across sensor modalities, allowing direct use in multimodal analysis. By combining heterogeneous sensors with standardized machine data, the MSM dataset provides a reusable benchmark for artificial intelligence (AI)-based monitoring, anomaly detection, and research in smart manufacturing.