ArticleScientific data2026
A dataset of measured machining deviations of compressor rotor blades.
Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- A dataset of measured machining deviations of compressor rotor blades.Scientific data · 2026Article
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The uncertainty in blade machining deviations leads to the offset in the average performance and the performance scatter of aero-engine compressors, posing a threat to the safe operation of engines. Therefore, quantifying the uncertainty effects of machining deviations is critically important. However, due to factors such as prolonged inspection cycles and high costs, geometric data on blade machining deviations remain scarce. Most uncertainty quantification analyses are conducted under assumed statistical distributions of deviations, making it difficult to guarantee the accuracy of the quantification. In this paper, a dataset of measured machining deviations of 100 compressor rotor blades is presented. And it includes 7 types of machining deviations of 13 blade sections from blade root to tip. The work fills a critical gap in available geometric deviation data for compressor rotor blades, and provides a reliable foundation for subsequent uncertainty quantification investigation.
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Registered trials
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