# Study on a Process Parameter-Driven Deep Learning Prediction Model for Multi-Physical Fields in Flange Shaft Welding

## TL;DR

This study develops a deep learning model to predict welding temperature, deformation, and stress in large flange shafts, showing that MLP performs best with low prediction errors.

## Contribution

The paper introduces a novel MLP-based model for multi-physical field prediction in welding, achieving high accuracy and practical applicability.

## Key findings

- The MLP model achieved prediction errors below 5% for temperature and within 10% for deformation and residual stress.
- RF showed good accuracy with an average peak residual stress error of about 6.5 MPa.
- XGBoost and FT-Transformer had weak generalization and higher prediction errors under small-sample conditions.

## Abstract

Large flange shafts are the core load-bearing and connecting components of high-end equipment, and their welding multi-physical fields directly affect the quality and service safety of the components. Traditional experiments and finite element methods suffer from long cycles and low efficiency, which can hardly meet the demand for rapid prediction. Aiming at the fast and accurate prediction of welding temperature, deformation and residual stress, this study combines thermal–mechanical coupled finite element simulation with machine learning to construct and compare a variety of prediction models. A dataset is built based on simulation data from 100 groups of process parameters. Overfitting is reduced through strategies including early stopping and dropout, and models such as MLP, RF, RBF-SVR, TabNet, XGBoost, and FT-Transformer are established and verified through 10-fold cross-validation. The results show that the MLP model performs best in the prediction of temperature, deformation and residual stress, and is in good agreement with the simulation values. The prediction errors of the peak temperature of the weld and base metal are below 5%, and the errors of deformation and residual stress are controlled within 10%. The average error of peak residual stress is about 6 MPa, and the deviation of most samples is less than 5 MPa. The RF model ranks second in accuracy, with an average error of about 6.5 MPa for peak residual stress, showing a satisfactory interpretability and engineering applicability. RBF-SVR and TabNet can meet basic prediction requirements. Under the small-sample condition in this work, XGBoost and FT-Transformer present relatively large errors and a weak generalization ability, making it difficult to achieve high-precision prediction. The MLP model established in this paper can effectively reproduce the evolution of welding multi-physical fields and supports the rapid prediction and process optimization of large flange shaft welding. The generalization ability and practical performance of the model can be further improved by expanding the dataset and experimental verification in the future.