Tool wear dataset of NUAA_Ideahouse

This dataset is used for i) analyzing the influence of process information on monitoring signals through signal processing methods; ii) training and testing models of tool monitoring and tool wear prediction especially for cutting conditions with large variations including cutting parameters, material and geometry of cutting tools, and workpiece materials, and also cutting conditions with continuous changes. This data set includes monitoring signals collected from machining process of sidewalls and closed pockets. The sidewall machining belongs to the cutting process with fixed cutting conditions; the closed pocket machining belongs to the cutting process of continuously varying cutting conditions for the reason that the tool path of closed pocket includes line, arc, full cutting and non-full cutting. Although cutting parameters are given fixed in the arc tool path area, the actual cutting parameters (such as feed, cutting width) are constantly changing due to the change of cutting geometry.

NUAA_Ideahouse data set is tool wear data under variable cutting conditions, the copyright is reserved by NUAA Ideahouse, when you use this data, please also refer the following paper, where the dataset is firstly published: "Liu C, Li Y, Li J, Hua J. A meta-invariant feature space method for accurate tool wear prediction under cross-conditions[J]. IEEE Transactions on Industrial Informatics, 2021. "