Abstract
This dataset represents a triaxial vibration dataset acquired during a face‑milling process under two distinct tool conditions (sharp and worn). Experiments were conducted on a 3‑axis CNC milling machine (VECTOR 850 M SI) using a 10 mm solid carbide end mill with 6 flutes, machining a flat steel workpiece in conventional face milling. The milling feed direction was aligned with the machine x‑axis. Cutting parameters were kept constant across both tool conditions: 60 % radial depth of cut, 2 mm axial depth of cut, spindle speed of 3500 rpm, and feed rate of 600 mm/min.
Vibrations were measured with a Bosch BMI270 MEMS accelerometer mounted on the spindle housing. The sensor axes were aligned with the machine coordinate system (x: feed direction, y: transverse in‑plane, z: spindle axis). Triaxial acceleration signals were recorded as raw time series at a sampling frequency of 1600 Hz and are provided in units of g. The dataset consists of two NumPy .npy files, one for each tool condition: SharpTool_1600Hz_xyz.npy (sharp tool) and WornTool_1600Hz_xyz.npy (worn tool). Each file contains a 2D NumPy array of shape (n_samples, 3), corresponding to time samples and the three acceleration components (x, y, z). No additional label files are required, as the tool condition is encoded in the file name. The dataset is intended for developing and benchmarking signal processing and machine learning methods for tool wear detection and related diagnostics in milling operations.
