Research and development in the field of metal-based additive manufacturing are advancing steadily every year. In order to increase the efficiency of powder bed fusion of metals using a laser beam system (PBF LB/M), machine manufacturers have implemented extensive optimizations with regard to the laser systems and build volumes. However, the optimization of metallic powder materials using nanoparticle additives enables an additional improvement of the laser–material interaction. In this work, tool steel 1.2709 powder was coated with silicon carbide (SiC), few-layer graphene (FLG), and iron oxide black (IOB) on a nanometer scale. Subsequently, the feedstock material and the modified powder materials were analyzed concerning the reflectance of the laser radiation and processed by PBF-LB/M in a systematic and consistent procedure to evaluate the impact of the nano-additivation on the process efficiency and mechanical properties. As a result, an increased build rate is achieved, exhibiting a relative density of 99.9% for FLG/1.2709 due to a decreased reflectance of this modified powder material. Furthermore, FLG/1.2709 provides hardness values after precipitation hardening with only aging comparable to the original 1.2709 material and is higher than the SiC- and IOB-coated material. Additionally, the IOB coating tends to promote oxide-formation and lack-of-fusion defects.
In the past decade, the sales of metal additive manufacturing systems have increased intensely. In particular, PBF-LB/M systems (powder bed fusion of metals using a laser-based system) represent a technology of great industrial interest, in which metallic powders are molten and solidified layer upon layer by a focused laser beam. This leads to a simultaneous increase in demand for metallic powder materials. Due to adjusted process parameters of PBF-LB/M systems, the powder is usually procured by the system's manufacturer. The requirement and freedom to process different feedstocks in a reproducible quality and the economic and ecological factors involved are reasons to have a closer look at the differences between the quality of the provided metallic powders. Besides, different feedstock materials require different energy inputs, allowing a sustainable process control to be established. In this work, powder quality of stainless steel 1.4404 and the effects during the processing of metallic powders that are nominally the same were analyzed and the influence on the build process followed by the final part quality was investigated. Thus, a correlation between morphology, particle size distribution, absorptivity, flowability, and densification depending on process parameters was demonstrated. Optimized exposure parameters to ensure a more sustainable and energy and cost-efficient manufacturing process were determined.Sustainability 2020, 12, 1565 2 of 14 useful for medical devices, e.g., surgical devices with thin-walled structures and, alongside Ti and Co-Cr alloys, for medical implants [4,5]. The ability to create complex lattice structures has resulted in a redesign of existing stainless steel components and lightweight structures in the automotive and aerospace industry to ensure more efficient and sustainable components [5].However, in order to ensure sustainable production, the entire process chain must be taken into consideration. Starting with powder production, through transportation and processing of the powder in the PBF-LB/M system, up to the use of a durable component and recycling of the nonfused metal powder. Kellens et al. [6] emphasize that energy consumption during the PBF-LB/M process is dominated by laser exposure. This was confirmed by Liu et al. [7], who state that energy consumption depends on exposure parameters such as laser power, scanning speed, layer thickness, etc. on the process level. Thus, the correlation between powder characteristics, the PBF-LB/M-process itself, and the final part quality represent a significant area of focus for sustainable process management.Great research efforts in the scientific community concerning stainless steel 1.4404 have addressed the correlation between input parameters (e.g., particle size distribution, chemical composition, process parameters, etc.) and output parameters (e.g., mechanical properties, relative density, microstructure, etc.). Process parameters consist of a large number of variables, including laser power, scanning speed, layer thickness...
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