Machine Learning Models and Data for the Application of Machine Learning in Predicting Quality Parameters in Metal Material Extrusion (MEX/M)

The supplementary data includes detailed information on machine learning (ML) models, specifically MLP and Bagging, and the datasets used to predict surface roughness and density in metal extrusion additive manufacturing (MEX/M) components. Leveraging experimental data, these models incorporate input parameters like layer thickness, print velocity, infill, overhang angle, and sinter profile. Demonstrating a prediction accuracy ranging from 39% to 97%, the data underscores the models' effectiveness in optimizing MEX/M processes, enhancing quality control, and improving design efficiency, particularly for complex geometrical structures.