ArticleScientific data2025
A multi-feature dataset of coated end milling cutter tool wear whole life cycle.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
5 citing papers in PubMed.
- Multi-sensor and MTConnect dataset of metal cutting anomaly in milling from laboratory and industry settings.Scientific data · 2026Article
- A Sensor based turning dataset for data-driven surface roughness estimation.Scientific data · 2026Article
- A dataset of measured machining deviations of compressor rotor blades.Scientific data · 2026Article
- Nonastreda multimodal dataset for efficient tool wear state monitoring.Data in brief · 2025Article
- A multi-feature dataset of coated end milling cutter tool wear whole life cycle.Scientific data · 2025Article
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Authors and funding
6 authors.
Funding
Abstract
Deep learning methods have shown significant potential in tool wear lifecycle analysis. However, there are fewer open source datasets due to the high cost of data collection and equipment time investment. Existing datasets often fail to capture cutting force changes directly. This paper introduces QIT-CEMC, a comprehensive dataset for the full lifecycle of titanium (Ti6Al4V) tool wear. QIT-CEMC utilizes complex circumferential milling paths and employs a rotary dynamometer to directly measure cutting force and torque, alongside multidimensional data from initial wear to severe wear. The dataset consists of 68 different samples with approximately 5 million rows each, includes vibration, sound, cutting force and torque. Detailed wear pictures and measurement values are also provided. It is a valuable resource for time series prediction, anomaly detection, and tool wear studies. We believe QIT-CEMC will be a crucial resource for smart manufacturing research.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.