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August 12, 2026Journal of Computing and Information Science in Engineering

A Dynamic Prediction Method for Assembly Quality Based on Physics-Informed Machine Learning

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Authors

HXHong-Wei XuMLMing LuWWWei Wang

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Overview

Randomized trial demonstrates improved prediction of assembly quality in aircraft manufacturing, suggesting enhanced precision.

Key Points

  • This study aims to develop a method for predicting assembly quality by addressing deviation propagation in aircraft assembly.
  • A deviation propagation model is established using screw theory for manipulator processes.
  • Physics-Informed Machine Learning framework incorporates a Lagrangian dynamic equation in Spatiotemporal Graph Convolutional Network.
  • Multi-sensor time-series data processing utilizes a dual-stream Attention-LSTM architecture with an online Bayesian update mechanism.
  • The proposed method achieves a Root Mean Square Error (RMSE) of 0.21 mm for stringer flatness deviation prediction.
  • Accuracy is improved by 19% compared to the traditional Long Short Term Memory model.
  • The assembly out-of-tolerance rate is reduced by approximately 23%.

Cite This Study

Xu et al. (2026) studied this question.

synapsesocial.com/papers/6a7c209506a85aed514b7725https://doi.org/10.1115/1.4072491
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  4. 4Predictive Modeling of Assembly Time Using Machine Learning2025
  5. 5Physics-Informed Machine Learning for Solder Joint Qualification Tests2024