Searching optimal process parameters for desired layer geometry in wire-laser directed energy deposition based on machine learning

The quantitative prediction of more process parameter variables for fewer layer geometry variables is challenging in wire-laser DED. This study's novelty is combining machine learning models with a non-dominated sorting genetic algorithm-II (NSGA-II) to predict process parameters for desired layer geometries accurately. Thirty single-layer deposition experiments are conducted to obtain response data of layer geometries to process parameters. Two support vector regression (SVR) models are trained by these data to predict the layer height and width, respectively, and the mean absolute percentage errors (MAPEs) of these models are 4.16% and 1.76%. A reverse system, consisting of both SVR models and the NSGA-II algorithm, is designed to search the optimal process parameters for the desired layer geometries. The maximum MAPE between the actual layer geometry deposited by the predicted process parameters and the desired layer geometry is less than 5.5%, providing solid confirmation of this methodology's reliability.