Increasing density and mechanical performance of binder jetting processing through bimodal particle size distribution

Ana Paula Clares, Yawei Gao, Ryan Stebbins, Adri C.T. van Duin, Guha Manogharan

Article ID: 20
Vol 1, Issue 3, 2022, Article identifier:20


Binder jetting is an additive manufacturing (AM) technology that has gained popularity and attention in recent years for production applications in tooling, biomedical, energy, and defense sectors. When compared to other powder bed fusion-based AM methods, binder jetting processes powder feedstock without the need of an energy source during printing. This avoids defects associated with melting, residual stresses, and rapid solidification within the parts. However, one of the challenges of this process is the relatively lower densities which impacts part density, and subsequently, sintering and mechanical properties. In this study, we investigated the influence of bimodal powder size distributions (a mixture of coarse to fine particles) as a method for increasing part density and mechanical strength, and used stainless steel (SS) 316L bimodal mixtures in this case. Four unimodal and two bimodal groups were evaluated under similar AM processing conditions for sintered density measurements and flexural strengths. Our results demonstrated that bimodal size distributions showed a statistically significant increase in density by 20% and ultimate flexural strength by 170% when compared to the highest performing unimodal group. In addition to experimental findings, reactive molecular dynamics simulations showed that the presence of finer powders along with coarser particles in the bimodal particle mixture contribute to additional bonds that are stronger across the particle interfaces. Findings from this study can be used to design bimodal particle size distributions to achieve higher density and better mechanical properties in binder jetting AM process.


Binder jetting; Bimodal powder; Mechanical strength; Molecular dynamics; 316L stainless steel; Additive manufacturing


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