Multivariate time series data of milling processes with varying tool wear and machine tools

Published: 5 November 2023| Version 3 | DOI: 10.17632/zpxs87bjt8.3
Tobias Stiehl


The presented dataset provides labeled, multivariate time series data of milling processes with varying tool wear and for varying machine tools. The width of the flank wear land VB of peripheral cutting edges is used as a degradation metric. A total of nine end milling cutters were worn from an unused state to end of life (VB ≈ 150 μm) in 3-axis shoulder milling of cast iron 600-3/S. The width of the flank wear land VB was frequently measured with a digital microscope at a magnification of 100x. The tools were of the same model (solid carbide end milling cutter, 4 edges, coated with TiN-TiAlN) but from different batches. Experiments were conducted on three different 5-axis milling centers of a similar size. Workpieces, experimental setups, and process parameters were identical on all of the machine tools. The process forces were recorded with a dynamometer with a sample rate of 25 kHz. The force or torque of the spindle and feed drives, as well as the position control deviation of feed drives, were recorded from the machine tool controls with a sample rate of 500 Hz. The dataset holds a total of 6,418 files labeled with the wear (VB), machine tool (M), tool (T), run (R), and cumulated tool contact time (C). The file “filelist.csv” provides an overview of all the sample files and their corresponding labels. This data could be used to identify signal features that are sensitive to tool wear, to investigate methods for tool wear estimation and tool life prediction, or to examine transfer learning strategies.



Leibniz Universitat Hannover


Cutting Tool, Milling, Machine Tool, Machining, Abrasive Wear, Time Series, Production Engineering, Transfer Learning, Remaining Useful Life


Bundesministerium für Wirtschaft und Klimaschutz