Areal Surface Roughness Optimization of Maraging Steel Parts Produced by Hybrid Additive Manufacturing

Abstract

We report on an experimental study and statistical optimization of the surface roughness using design of experiments and the Taguchi method for parts made of 1.2709 maraging steel. We employ a hybrid additive manufacturing approach that combines additive manufacturing by selective laser melting with subtractive manufacturing using milling in an automated process within a single machine. Input parameters such as laser power, scan speed, and hatching distance have been varied in order to improve surface quality of unmachined surfaces. Cutting speed, feed per tooth, and radial depth of cut have been varied to optimize surface roughness of the milled surfaces. The surfaces of the samples were characterized using 3D profilometry. Scan speed was determined as the most important parameter for non-machined surfaces; radial depth of cut was found to be the most significant parameter for milled surfaces. Areal surface roughness S a could be reduced by up to 40% for unmachined samples and by 23% for milled samples as compared to the prior state of the art.