# Adaptive Planning Method for ERS Point Layout in Aircraft Assembly Driven by Physics-Based Data-Driven Surrogate Model

## TL;DR

This paper introduces a method to optimize ERS point layout in aircraft assembly using a physics-based data-driven model, improving accuracy and reducing errors.

## Contribution

A novel adaptive planning method combining physics-based modeling and data-driven optimization for ERS point layout in aircraft assembly.

## Key findings

- The method reduces Registration-Induced Error (RIE) by approximately 40% compared to a uniform baseline.
- Registration-Induced Loss Ratio (RILR) is maintained at about 10%.
- The approach enables millisecond-level sensitivity prediction and avoids occlusion effectively.

## Abstract

In digital-measurement-assisted assembly of large aircraft components, the spatial layout of Enhanced Reference System (ERS) points determines coordinate transformation accuracy and stability. To address manual layout limitations—specifically low efficiency, occlusion susceptibility, and physical deployment limitations—this paper proposes an adaptive planning method under engineering constraints. First, based on the Guide to the Expression of Uncertainty in Measurement (GUM) and weighted least squares, an analytical transformation sensitivity model is constructed. Subsequently, a multi-scale sample library generated via Monte Carlo sampling trains a high-precision BP neural network surrogate model, enabling millisecond-level sensitivity prediction. Combining this with ray-tracing occlusion detection, a weighted genetic algorithm optimizes transformation sensitivity, spatial uniformity, and station distance within feasible ground and tooling regions. Experimental results indicate that the method effectively avoids occlusion. Specifically, the Registration-Induced Error (RIE) is controlled at approximately 0.002 mm, and the Registration-Induced Loss Ratio (RILR) is maintained at about 10%. Crucially, comparative verification reveals an RIE reduction of approximately 40% compared to a feasible uniform baseline, proving that physics-based data-driven optimization yields superior accuracy over intuitive geometric distribution. By ensuring strict adherence to engineering constraints, this method offers a reliable solution that significantly enhances measurement reliability, providing solid theoretical support for automated digital twin construction.