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Atomic Emission Spectroscopy: Interference01:30

Atomic Emission Spectroscopy: Interference

In atomic emission spectroscopy (AES), high-temperature atomizers excite a broad range of elements and molecules that generate complex emissions from sources such as oxides, hydroxides, and flame combustion products in the flame or plasma. Several strategies can be employed to minimize spectral interferences caused by overlapping emission lines or bands. These include increasing instrument resolution, choosing alternative emission lines, optimally placing the detector in low-background regions,...
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SFCF-Net: Spatial-Frequency Synergistic Learning for Casting Defect Segmentation of Pre-Service Aircraft Engine

Shun Wang1, Zhiying Sun1, Xifeng Fang1

  • 1School of Mechanical Engineering, Jiangsu University of Science and Technology, Zhenjiang 212003, China.

Sensors (Basel, Switzerland)
|March 14, 2026
PubMed
Summary

A new network, SFCF-Net, improves aircraft engine safety by accurately detecting turbine blade casting defects. It combines spatial and frequency domain features to overcome limitations of existing methods for better defect segmentation.

Keywords:
asymmetric window attentioncasting defect detectioncross-modal refinement and complementationselective cross-modal calibration

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Area of Science:

  • Materials Science and Engineering
  • Computer Vision and Artificial Intelligence
  • Aerospace Engineering

Background:

  • Turbine blades are vital aircraft engine components, but manufacturing defects pose safety risks.
  • Current deep learning defect detection methods struggle with poor image quality, varied defect appearances, and complex geometries.
  • Reliance on spatial-domain features limits the detection of subtle, texture-based defects.

Purpose of the Study:

  • To develop an advanced deep learning model for accurate turbine blade defect detection and segmentation.
  • To address the limitations of existing methods in handling poor image quality and complex defect patterns.
  • To enhance aircraft engine safety through improved automated quality control.

Main Methods:

  • Proposed the Spatial-Frequency Complementary Fusion Network (SFCF-Net) integrating spatial and frequency domain features.
  • Introduced a Selective Cross-modal Calibration (SCC) module for preserving fine-grained details under poor image conditions.
  • Developed a Cross-modal Refinement and Complementation (CRC) module with attention mechanisms for robust defect discrimination and a novel Asymmetric Window Attention (AWA) module for geometric characterization.

Main Results:

  • SFCF-Net demonstrated superior performance in defect segmentation compared to state-of-the-art methods on the ATBCD-Seg dataset and a public benchmark.
  • The proposed modules effectively handled poor image quality, intraclass variance, interclass similarity, and irregular defect shapes.
  • Achieved high accuracy in discriminating between similar defect categories while maintaining intra-class consistency.

Conclusions:

  • SFCF-Net offers a robust and accurate solution for turbine blade casting defect segmentation.
  • The synergistic fusion of spatial and frequency domain features is effective for complex defect detection.
  • The method meets practical requirements for automated quality control in aircraft engine manufacturing.