A Prediction Model for Additive Manufacturing of AlSi10Mg Alloy

The surface quality and mechanical properties of AlSi10Mg parts manufactured using laser powder bed fusion (LPBF) rely heavily on the process parameters. Such needs to be tuned to improve the overall reliability of the LPBF devices. To model and forecast the performance of the process, an artificial neural network (ANN) model with feedforward backpropagation algorithm was created and tested. Wherein, laser power (LP), point distance (PD) and exposure time (ET), and surface roughness ( S a ), Vickers microhardness ( H v ) were introduced as inputs and outputs of the network, respectively. This ANN tool will also be beneficial for dealing with the optimization of process parameters. Comparing the consequential ANN networks, it was discovered that, ANN architecture using "trainlm," "tansig"–"Logsig," as the training algorithm and transfer functions in the hidden and output layers, respectively, with eight hidden neurons and 400 training epoch, produces the best simulation result with the lowest mean square error (MSE). A model is developed to predict the optimal process parameters for producing AlSi10Mg components with desired surface roughness and Vickers microhardness.