Process parameters optimisation for selective laser melting of AlSi10Mg-316L multi-materials using machine learning method

The present work focuses on process parameters optimisation for selective laser melting (SLM) of AlSi10Mg-316L multi-materials using machine learning method. The mechanical properties of the multi-material samples were measured at different process parameters. These process parameters and properties data were used to train and validate the machine learning model. A multi-output Gaussian process regression (MO-GPR) model was developed to directly predict the multidimensional output to overcome the limitations of the standard Gaussian process regression (GPR) model. Based on the prediction data, process parameter maps were constructed, and the optimal process parameters for different compositions were selected from the process parameter maps. The results showed that the laser power, scan velocity and hatching space have an important influence on the density and surface roughness of the samples. Results also indicated that there is no linear functional relationship between the optimal volumetric energy density (VED) values and the AlSi10Mg-316L compositions.