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Summary
This summary is machine-generated.

This study introduces an adaptive planning method for Enhanced Reference System (ERS) point layout in aircraft assembly, improving accuracy and efficiency. The physics-based, data-driven approach significantly reduces measurement errors compared to traditional methods.

Keywords:
BP neural networkaircraft assemblycoordinate transformation uncertaintyenhanced reference system (ERS)laser tracker

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Area of Science:

  • Aerospace Engineering
  • Metrology
  • Computational Geometry

Background:

  • Manual layout of Enhanced Reference System (ERS) points in aircraft assembly is inefficient and prone to occlusion.
  • Accurate spatial layout of ERS points is critical for coordinate transformation accuracy and stability in digital-measurement-assisted assembly.

Purpose of the Study:

  • To propose an adaptive planning method for ERS point layout that overcomes manual limitations.
  • To optimize ERS point placement considering engineering constraints, transformation sensitivity, spatial uniformity, and occlusion avoidance.

Main Methods:

  • Constructed an analytical transformation sensitivity model using the Guide to the Expression of Uncertainty in Measurement (GUM) and weighted least squares.
  • Developed a high-precision BP neural network surrogate model trained on Monte Carlo samples for millisecond-level sensitivity prediction.
  • Employed a weighted genetic algorithm combined with ray-tracing occlusion detection for optimization within feasible ground and tooling regions.

Main Results:

  • The adaptive method effectively avoids occlusion, controlling Registration-Induced Error (RIE) to approximately 0.002 mm.
  • Registration-Induced Loss Ratio (RILR) was maintained at about 10%.
  • Achieved an approximate 40% reduction in RIE compared to a uniform baseline, demonstrating superior accuracy.

Conclusions:

  • The physics-based, data-driven optimization method significantly enhances measurement reliability in aircraft assembly.
  • The approach adheres to engineering constraints, offering a reliable solution for automated digital twin construction.
  • This method provides a robust theoretical foundation for improving digital measurement-assisted assembly processes.