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Typical Model Studies

Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.

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Related Experiment Video

Updated: Jun 29, 2026

Ultrasonic Welding of Thermoplastic Composite Coupons for Mechanical Characterization of Welded Joints through Single Lap Shear Testing

Ultrasonic Welding of Thermoplastic Composite Coupons for Mechanical Characterization of Welded Joints through Single Lap Shear Testing

Published on: February 11, 2016

Prediction of Electron Beam Welding Penetration Depth Using Machine Learning-Enhanced Computational Fluid Dynamics

Yi Yin^1,2, Yingtao Tian¹, Jialuo Ding²

¹Department of Engineering, Lancaster University, Bailrigg, Lancaster LA1 4YW, UK.

Sensors (Basel, Switzerland)

|November 14, 2023

Summary

This summary is machine-generated.

This study presents a new method for predicting electron beam welding (EBW) penetration depth by combining computational fluid dynamics (CFD) and artificial neural networks (ANN). This efficient approach reduces costs and improves weld quality control.

Keywords:

artificial neural networks beam characterisation computational fluid dynamics modelling electron beam welding machine learning penetration depth prediction

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Surrogate Model Development for Digital Experiments in Welding

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Published on: March 28, 2025

Area of Science:

Materials Science and Engineering
Computational Physics
Manufacturing Technology

Background:

Precise prediction of electron beam welding (EBW) penetration depth is crucial for quality control.
Traditional methods like regression analysis and neural networks can be time-consuming and expensive.
Existing predictive models often lack efficiency and require extensive preliminary testing.

Purpose of the Study:

To develop a novel, efficient, and accurate approach for predicting EBW penetration depth.
To integrate computational fluid dynamics (CFD) modelling with artificial neural networks (ANN) for enhanced predictive accuracy.
To reduce the time and financial resources required for EBW process optimization.

Main Methods:

Synergistic combination of computational fluid dynamics (CFD) modelling and artificial neural networks (ANN).
Application of CFD for simulating the physical processes involved in EBW.
Training and validation of ANN models using CFD-generated data and experimental parameters.
Development of a beam characterisation method for broader applicability.

Main Results:

The integrated CFD-ANN approach achieved high accuracy in predicting EBW penetration depth, with an average absolute percentage deviation of approximately 8%.
Accurate predictions were consistent across a linear electron beam power range of 86 J/mm to 324 J/mm.
The method significantly reduces the need for costly and time-intensive preliminary testing.

Conclusions:

The combined CFD-ANN model offers an efficient and accurate solution for predicting EBW penetration depth.
This approach enhances control over EBW weld quality by enabling fine-tuning of key process variables.
The methodology is adaptable to different electron beam machines, offering versatile application in industrial settings.