Development of a universal multimodal prediction method to optimise process parameters for improving densification during laser powder bed fusion

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ABSTRACT This work develops a universal machine learning method that enhances the densification of metallic materials fabricated by laser powder bed fusion (LPBF) by optimising process parameters using multimodal data. By integrating ResNet-50 neural network and LightGBM model, this method combines process parameters with surface morphology of LPBF-fabricated samples at the feature scale, enabling extraction of multidimensional feature information and improving prediction accuracy of relative density. Statistical analysis demonstrates exceptional performance of the model on both training and testing datasets, yielding coefficient of determination (R²) values of 0.9821 and 0.8169, and mean absolute error values of 0.0178 and 0.0108, respectively. Bayesian optimisation was employed using the multimodal model as a surrogate model to identify potential optimal combinations within the parameter space. These results validate the effectiveness of establishing a multimodal machine learning method based on multimodal data sources to optimise LPBF process parameters and improve relative density of fabricated new materials.