Additive manufacturing of steel using arc welding involves complex thermal cycles during the melting and solidification processes. This results in unpredictable and heterogeneous microstructures and mechanical properties of the component. Algorithms for thermal, metallurgical, and mechanical simulations are combined with machine learning and metaheuristic optimization techniques to optimize process parameters, reduce experimental costs, and improve the reliability of predictions.