Surrogate Model Development for Digital Experiments in Welding.

Area of Science:

• Materials Science and Engineering
• Computational Mechanics
• Manufacturing Processes

Background:

• Welding is critical in manufacturing, but welding-induced residual stress impacts structural integrity.
• Predicting residual stress is vital for reliable welded structures.
• Traditional simulations are time-consuming, hindering rapid assessment.

Purpose of the Study:

• To develop an efficient workflow for predicting welding-induced residual stress using artificial neural networks (ANNs).
• To automate data generation from finite element simulations for ANN training.
• To reduce the time and effort associated with traditional simulation methods.

Main Methods:

• Constructing and validating a standard weld finite element simulation.
• Developing Python scripts with macro functions for automated data generation.
• Training and testing an ANN-based surrogate model on generated data.
• Validating simulation results against experimental data.

Main Results:

• The automated workflow significantly reduces simulation setup and data extraction time.
• ANN surrogate models achieved high accuracy in predicting residual stress.
• The relative root mean square error was 0.0024, indicating close alignment with simulation results.

Conclusions:

• The developed workflow offers an efficient and reproducible method for predicting welding-induced residual stress.
• ANN-based surrogate models provide a fast and accurate alternative to traditional simulations.
• This approach enhances the reliability and durability of welded components in manufacturing.