1. Systematic approach to process parameter optimization for laser powder bed fusion of low-alloy steel based on melting modes
- Author
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Simon Bergmueller, Lukas Gerhold, Lorenz Fuchs, Lukas Kaserer, and Gerhard Leichtfried
- Subjects
Control and Systems Engineering ,Mechanical Engineering ,Industrial and Manufacturing Engineering ,Software ,Computer Science Applications - Abstract
In the metal additive manufacturing (AM) process of laser powder bed fusion (LPBF), there are a limited number of materials suitable for producing parts with high density and desired mechanical properties. To establish novel materials, it is essential to determine optimized process parameters in order to overcome process-related challenges and mitigate defects such as lack of fusion, keyholing, and balling. Scaling laws based on thermophysical properties and process parameters can be used to transfer knowledge from other materials or LPBF systems. In this work, a scaling law is used to adjust process parameters for single-track experiments over a wide range, which are laser power PL (100–1000 W), scan speed vs (300–2500 mm/s), and laser spot size ds (0.08–0.25 mm). Compared to existing studies, the parameter range is thus extended towards large laser spot sizes and high laser powers. The scaling law used is based on the calculation of the normalized enthalpy $$\frac{\Delta H}{{h}_{s}}$$ Δ H h s . The ratio of the deposited energy density $$\Delta$$ Δ H and the melting enthalpy hs correlates with the dimensions of the melt pool. According to the aspect ratio $${\delta }_{\mathrm{c}}$$ δ c of the melt pool of each single track, the respective melting mode—conduction, transition, and keyhole mode—was identified. The process parameters of the single tracks in transition mode were used to optimize the density of the LPBF specimens with varying hatch distance hd (0.06–0.12 mm), resulting in specimens with a relative density of > 99.8%. The proposed methodology can accelerate the process parameter finding for new alloys and avoid process-related defects. Graphical Abstract
- Published
- 2023
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