Asami, KarimKarimAsamiKuehne, MaximMaximKuehneRöver, TimTimRöverEmmelmann, ClausClausEmmelmann2025-06-032025-06-032025-04-30Metals 15 (5): 505 (2025)https://hdl.handle.net/11420/55707Additive manufacturing processes such as the material extrusion of metals (MEX/M) enable the production of complex and functional parts that are not feasible to create through traditional manufacturing methods. However, achieving high-quality MEX/M parts requires significant experimental and financial investments for suitable parameter development. In response, this study explores the application of machine learning (ML) to predict the surface roughness and density in MEX/M components. The various models are trained with experimental data using input parameters such as layer thickness, print velocity, infill, overhang angle, and sinter profile enabling precise predictions of surface roughness and density. The various ML models demonstrate an accuracy of up to 97% after training. In conclusion, this research showcases the potential of ML in enhancing the efficiency in control over component quality during the design phase, addressing challenges in metallic additive manufacturing, and facilitating exact control and optimization of the MEX/M process, especially for complex geometrical structures.en2075-470120255Multidisciplinary Digital Publishing Institutehttps://creativecommons.org/licenses/by/4.0/additive manufacturing (AM) | material extrusion of metals (MEX/M) | machine learning (ML) | process development | AISI stainless steel 1.4404/316L | design for additive manufacturing (DfAM)Technology::670: ManufacturingComputer Science, Information and General Works::006: Special computer methodsTechnology::620: Engineering::620.1: Engineering Mechanics and Materials Science::620.11: Engineering MaterialsJournal Article2025-05-27https://doi.org/10.15480/882.1521310.3390/met1505050510.15480/882.15213Journal Article