Adaptive machining framework for the leading/trailing edge of near-net-shape integrated impeller

2020 ◽  
Vol 107 (9-10) ◽  
pp. 4221-4229 ◽  
Author(s):  
Yun Zhang ◽  
Zhitong Chen ◽  
Zhengqing Zhu
Keyword(s):  
Trailing Edge ◽  
Adaptive Machining ◽  
Near Net Shape

scholarly journals LETR: An End-to-End Detector of Reconstruction Area in Blade’s Adaptive Machining with Transformer

2022 ◽  
Author(s):  
Zikai Yin ◽  
Yonghou Liang ◽  
Junxue Ren ◽  
Jungang An ◽  
Famei He
Keyword(s):  
Activation Function ◽  
Aerodynamic Performance ◽  
Image Features ◽  
The Self ◽  
Test Results ◽  
Baseline Model ◽  
Trailing Edge ◽  
Reconstruction Process ◽  
Mixed Frequency ◽  
Adaptive Machining

In the leading/trailing edge’s adaptive machining of the near-net-shaped blade, a small portion of the theoretical part is retained for securing aerodynamic performance by manual work. However, this procedure is time-consuming and depends on the human experience. In this paper, we defined retained theoretical leading/trailing edge as the reconstruction area. To accelerate the reconstruction process, an anchor-free neural network model based on Transformer was proposed, named LETR (Leading/trailing Edge Transformer). 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 GLETR (Ghost Leading/trailing Edge Transformer) 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.

scholarly journals Development of a Novel System for Adaptive Machining of Near-Net-Shape Components

2021 ◽  
Author(s):  
Zhengcai Zhao ◽  
Shengtao Lin ◽  
Yucan Fu
Keyword(s):  
Shape Reconstruction ◽  
Free Form ◽  
Nc Programming ◽  
Spatial Deformation ◽  
Manufacturing Efficiency ◽  
Adaptive Machining ◽  
Machining Allowance ◽  
Near Net Shape ◽  
Cutter Position

Near-net-shape components are popular among the aerospace industry for low material waste and high manufacturing efficiency. However, it is difficult to machine such components into final shapes because the machining allowance is often distributed unevenly and even insufficient. This paper proposed a novel system for adaptive machining near-net-shape components, which integrates units like on-machine measurement based on probe and ultrasonic-sensor, machining allowance constrained localization, tolerance range constrained shape reconstruction, and TCP (tool cutter position) template-based NC programming. Firstly, localization and free form deformation (FFD)-based shape construction are performed within the tolerance ranges of the component, and an even distribution of the machining allowance can be obtained. Next, the quick NC programming that directly manipulates the TCPs by using spatial deformation is introduced. Last, the data transmission between units is illustrated. A case study of the machining titanium turbine blade is performed, which validates the proposed system.

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