Nonastreda multimodal dataset for efficient tool wear state monitoring
Year: 2025
Type: data-paper
Abstract: for efficient tool wear state monitoring. The dataset models the multimodal nature of tool wear progression in industrial milling processes, integrating nine data modalities. It comprises 512 samples, each containing RGB images of the shaft milling tool, workpiece, and material chip, along with three scalograms and three spectrograms derived from force signals. Data collection was performed using ten milling tools in an industrial production environment. The dataset is designed to support classification tasks (sharp, used, dulled) and regression tasks predicting three target variables: flank wear (µm), gaps (µm), and overhang (µm). Each sample can be analyzed independently or as part of a temporally correlated sequence. Accompanying scripts for data processing and analysis are available in the repository.
Source: Data in Brief
Authors: Hubert Truchan, Zahra Ahmadi
Language: English
Subfield: Mechanical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,220
OpenAlex ID: W4412611777