Year: 2026
Type: data-paper
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.
Source: Scientific Data
Authors: Limin Gao, Yue Dan, Haohao Wang, Ruiyu Li, Guang Yang
Institution: Northwestern Polytechnical University
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,490
Awards: G2757073427, G7163711027, G7176090469
OpenAlex ID: W7129081771