Additive manufacturing can produce parts with complex geometries in fewer steps than conventional processing, which leads to cost reduction and a higher quality of goods. One potential application is the production of molds and dies with conformal cooling for injection molding, die casting, and...

Additive manufacturing can produce parts with complex geometries in fewer steps than conventional processing, which leads to cost reduction and a higher quality of goods. One potential application is the production of molds and dies with conformal cooling for injection molding, die casting, and forging. AISI H13 tool steel is typically used in these applications because of its high hardness at elevated temperatures, high wear resistance, and good toughness. However, available data on the processing of H13 steel by additive manufacturing are still scarce. Thus, this study focused on the processability of H13 tool steel by powder bed fusion and its microstructural characterization. Laser power (97−216 W) and scan speed (300−700 mm/s) were varied, and the consolidation of parts, common defects, solidification structure, microstructure, and hardness were evaluated. Over the range of processing parameters, microstructural features were mostly identical, consisting of a predominantly cellular solidification structure of martensite and 19.8 %–25.9 % of retained austenite. Cellular/dendritic solidification structure displayed C, Cr, and V segregation toward cell walls. The thermal cycle resulted in alternating layers of heat-affected zones, which varied somewhat in hardness and microstructure. Retained austenite was correlated to the solidification structure and displayed a preferential orientation with {001}//build direction. Density and porosity maps were obtained by helium gas pycnometry and light optical microscopy, respectively, and, along with linear crack density, were used to determine appropriate processing parameters for H13 tool steel. Thermal diffusivity, thermal conductivity, and thermal capacity were measured to determine dimensionless processing parameters, which were then compared to others reported in the literature

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