Establishing precise welding parameters is essential to achieving the desired bead geometry and ensuring consistent quality in manufacturing processes. However, determining the optimal configuration of parameters remains a challenge, particularly when relying on limited experimental data. This study proposes the use of artificial neural networks (ANNs), with their architecture optimized via differential evolution (DE), to predict key MAG welding parameters based on target bead geometry. To address data limitations, cross-validation and data augmentation techniques were employed to enhance model generalization. In addition to the ANN model, machine learning algorithms commonly recommended for small datasets, such as K-nearest neighbors (KNNs) and support vector machines (SVMs), were implemented for comparative evaluation. The results demonstrate that all models achieved good predictive performance, with SVM showing the highest accuracy among the techniques tested, reinforcing the value of integrating traditional ML models for benchmarking purposes in low-data scenarios.

To meet industry demands for more resistant materials with good mechanical properties and moderate cost, high strength low alloy steels (HSLA) were developed. However, the low ductility of these steels can be a problem. In addition, many components are subjected to MIG welding. Finding a proper heat treatment, able to modify the mechanical properties according to the design requirements and enhancing the material’s weldability is still an issue. The present work shows a microstructural and mechanical properties analysis of a HSLA steel, named DOMEX 460MC, subjected to three heat treatments before joining: 400 °C, 580 °C and 700 °C. Brinell hardness tests and microstructural analysis were carried out for each condition. Moreover, 5 tensile specimens were made according to the ASTM E8M-04 standard to assess the mechanical behavior. The results showed that both the previous heat treatments and welding could modify the microstructure, decreasing the material's mechanical strength.