Asami, Mohammad KarimMohammad KarimAsamiKuehne, MaximMaximKuehne2025-03-102025-03-102025-03-10https://hdl.handle.net/11420/54722The 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.enhttps://creativecommons.org/licenses/by/4.0/Additive Manufacturing (AM)Machine learning (ML)Material extrusion of metals (MEX/M)Design for Additive Manufacturing (DfAM)Computer Science, Information and General Works::006: Special computer methods::006.3: Artificial IntelligenceTechnology::620: Engineering::620.1: Engineering Mechanics and Materials ScienceCompiled Datahttps://doi.org/10.15480/882.1488010.15480/882.14880