Stream surface reconstruction of aero engine blade based on limited measured points

Lyu, Xuegeng; Liu, Peng

doi:10.1016/j.advengsoft.2019.02.008

Cited by 15 publications

(2 citation statements)

References 28 publications

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“…Feng et al [2] predicted the desired spline curves considering the deformation and thickness of the blank material while preserving the design intent. Yu et al [18] established the relationship between measured points and the velocity field of the blade's section. Further, they calculated the curves in the area without measuring data.…”

Section: Related Workmentioning

confidence: 99%

LETR: An End-to-End Detector of Reconstruction Area in Blades Adaptive Machining with Transformer

et al. 2022

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In the leading/trailing edge’s adaptive machining of the near-net-shaped blade, a small portion of the theoretical part, called the reconstruction area, is retained for securing aerodynamic performance by manual work. The next work is to recognize the reconstruction area of the reconstructed leading/trailing edge’s image. To accelerate this process, an anchor-free neural network model based on Transformer was proposed, named Leading/trailing Edge Transformer (LETR). LETR extracts image features from an aspect of mixed frequency and channel domain. We also integrated LETR with the newest meta-Acon activation function. We tested our model on the self-made dataset LDEG2021 on a single GPU and got an mAP of 91.9%, which surpassed our baseline model, Deformable DETR, by 1.1%. Furthermore, we modified LETR’s convolution layer and named the new model after Ghost Leading/trailing Edge Transformer (GLETR) as a lightweight model for real-time detection. It is proved that GLETR has fewer weight parameters and converges faster than LETR with an acceptable decrease in mAP (0.1%) by test results. The proposed models provide the basis for subsequent parameter extraction work in the reconstruction area.

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Section: Related Workmentioning

confidence: 99%

LETR: An End-to-End Detector of Reconstruction Area in Blades Adaptive Machining with Transformer

et al. 2022

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“…Nearly all studies use shape recording methods, but often, these are manual. For the described methods, the generated point clouds have to be reconstructed into curves or surfaces, for which some algorithms are described [29,30].…”

Section: Introductionmentioning

confidence: 99%

A Novel Process Chain for the Automated Repair of Leading Edges in Aircraft Engines

et al. 2022

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A novel model reconstruction strategy for polishing turbine blade based on position optimization of sectional curve

Zhu

Chen

Zhang

2022

Int J Adv Manuf Technol

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Stream surface reconstruction of aero engine blade based on limited measured points

Cited by 15 publications

References 28 publications

LETR: An End-to-End Detector of Reconstruction Area in Blades Adaptive Machining with Transformer

LETR: An End-to-End Detector of Reconstruction Area in Blades Adaptive Machining with Transformer

A Novel Process Chain for the Automated Repair of Leading Edges in Aircraft Engines

A novel model reconstruction strategy for polishing turbine blade based on position optimization of sectional curve

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