Material and process invariant scaling laws to predict porosity of dense and lattice structures in laser powder bed fusion

Scaling laws represent an efficient way to describe complex physical phenomena efficiently and reliably by simplified relative quantities, which are holistic in the sense that they are invariant to changes in scale and therefore often universal. During part manufacturing in laser powder bed fusion additive manufacturing several such complex physical phenomena arise leading to unstable melt pool behavior and part porosity. Correlating processing parameters with melt pool quantities and eventually part properties such as porosity computationally efficiently can enable real-time build failure detection and process adaption leading to zero scrap rates in additive manufacturing. The efficient and reliable process-property correlation for dense materials is an ongoing field of research, whereas architected cellular and lattice structures, which are becoming more and more relevant in the context of additive manufacturing are rarely considered in this regard. In this contribution, a dimensionless number and the corresponding scaling law are derived to describe the correlation between porosity and process parameters for components manufactured by laser powder bed fusion. The scaling law is tested and validated for the commonly used alloys Ti-6Al-4V and AlSi10Mg on both dense and lattice structures regarding its suitability to predict the type and amount of porosity in additively manufactured components. The objective of this work is to foster reliable and predictable part quality and enable quality-driven build rate optimization. •Introduction of a dimensionless number and its corresponding scaling law.•Testing and validating the scaling law for the widely used alloys Ti-6Al-4V and AlSi10Mg.•Demonstrating the ability to predict the type and amount of porosity in additively manufactured components.•Leads to reliable and predictable part quality as well as enabling quality-driven build rate optimization.