<p>This research focuses on searching suitable metal additive laser powder bed fusion machine parameters values for laser power, scan speed, layer thickness, baseplate temperature, hatch spacing, and laser beam diameter, aimed at manufacturing magnetic shields for wireless charging of electric vehicles. To achieve good quality components, it is important to minimize the residual stress and distortion of the components during the manufacturing process. AlSi10Mg alloy is used for the finite element analyses of the laser powder bed fusion process with varying parameters, and the simulated database is used for developing machine learning based surrogate models for the residual stress and the distortion of the component. These models are used as the objective functions for minimizing the residual stress and the distortion simultaneously using a multi-objective genetic algorithm. The artificial intelligence driven search provides the magnitudes of the process parameters capable of improving part quality, reduced defects, and enhanced mechanical properties.</p> Graphical abstract <p></p>