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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Artificial Neural Networks for the Optimization of Welding Parameters: Accurate Prediction Of Thermo-Mechanical Behaviour

Artificial Neural Networks for the Optimization of Welding Parameters: Accurate Prediction Of Thermo-Mechanical Behaviour

Belahcene, Brahim

Paperback

Series: Artificial Neural Networks for the Optimization of Welding Parameters, Book 1

Technology & EngineeringPhysics

ISBN13: 9798277985113
Publisher: Independently Published
Published: Dec 8 2025
Pages: 36
Weight: 0.13
Height: 0.10 Width: 6.00 Depth: 9.00
Language: English
The reliability of assembled structures is significantly influenced by the applied thermomechanical stresses and the robustness degree of the simulation numerical methods. The utilization of classical numerical methods such as the finite element method (FEM), extended finite element method XFEM, and mean weighted residuals method are computational costs due to the complexity of the materials behaviour laws, physicals mathematical model and laboratory apparatus cost. To ensure accurate investigation techniques, it should be performed a numerical model used for resolving welding physical equations governed. The main objective of this study is to architect and optimize an intelligent model based on an artificial neural network to resolve a complex model of the calculation effect of spot welding on the behaviour of HLE steel. The ANN model gives a strong correlation between the dataset as numeric input and the target. The artificial neuron network gives a proxy model approach to exploit input data and results extracted by simulation of weld spot using finite element method FEM. The performance evaluation of the ANN model was carried out using mean square error and regression analysis. As a result, the present model ANN gives with minimum computational cost a good match of temperature estimating, equivalent stress and strain along the contact area of two thin plates of steel studied assembled by weld spot with a comparison between classical models using FEM.

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