A multimodal dataset for process monitoring and anomaly detection in industrial CNC milling

During the fourth industrial revolution, agile production methods have gained increasing importance to meet the growing demand for product individualization. Conventional process monitoring systems, which predominantly rely on static, statistically based approaches, are insufficient for the requirements of flexible manufacturing environments. Although numerous research initiatives have proposed machine learning (ML) based solutions for agile process monitoring, widespread adoption in industrial practice has not yet been achieved. This is partly due to a lack of system comparability and insufficient validation under realistic production conditions. To address this, a comprehensive dataset was recorded on a DMC 60 H three-axis milling machine by Deckel Maho. The dataset comprises multiple signals from a Siemens SINUMERIK 840D controller, recorded at 500 Hz via a Siemens SINUMERIK Edge. These were synchronized with force and acceleration data (sampled at 10 kHz) captured via a force measurement platform and acceleration sensors. A total of 32 experiments were conducted (15 with 8 distinct anomaly types), resulting in nearly 8 million data points per signal and six hours of process data. Key features include: • Realistic workpiece geometries (thermoforming molds, injection molds, pump impellers) representing diverse milling scenarios. • Multiple anomalies (e.g., tool wear, chatter, material defects) to enable targeted validation. • Full reproducibility through provided NC codes, CAD models, and raw/processed data formats (.json, .mat, .csv, .stp, .nc). The dataset is intended to serve as a benchmark for industrial stakeholders to evaluate monitoring systems, as well as providing researchers with a secondary resource for benchmarking and optimizing ML-based approaches. By fostering comparability, it aims to bridge the gap between theoretical frameworks and industrial application.