Predictive models for an optimized fabrication of 18Ni300 maraging steel for moulding and tooling by Selective Laser Melting

Abstract

Powder bed fusion (PBF) technologies have gained increased attention in the automotive sector for the manufacturing of mould tooling and inserts. These technologies can expressively reduce lead time and waste of material, while allowing extraordinary freedom to design new geometries. The performance of the produced parts is highly dependent on processing parameters. In this work, 18Ni300 maraging steel, a widely used material in mould and tooling industries, was selected to be transformed by Selective Laser Melting (SLM) using a previous defined framework of SLM variables, among them laser power (Lp), point distance (Pd), exposure time (Et) and hatch distance (Hd). The experimental results demonstrated that these parameters have vital importance to produce fully dense and micro-hardness improved parts. Furthermore, results showed that the energy density per se does not explain the final properties of 18Ni300 produced by SLM. Maximized density (99.99%) was achieved using (Lp, Pd, Et, Hd) (275.0 W, 60 μm, 65.0 μs, 110 μm), corresponding to 2.71 J/mm2 planar energy density, while maximized micro-hardness (350 HV2) was achieved using (Lp, Pd, Et, Hd) (337.5 W, 70 μm, 52.5 μs, 95 μm), corresponding to 2.66 J/mm2 planar energy density. The statistical relationship between SLM parameters and final density and micro-hardness of the parts was established using the so-called Response Surface Methodology (RSM), resulting in two predictive models, for density and micro-hardness. The most influential (single and combined factors), for both models, were then determined using analysis of variance (ANOVA). The outcomes of the ANOVA analysis revealed a predicted coefficient of determination, R2(pred.), of 93.73% and 98.98% for density and micro-hardness models, respectively, revealing that the developed models have high accuracy for the prediction of both properties on 18Ni300 steel parts produced by SLM.