Introduction: Why Evaluation of the Spreadability is Essential in Additive Manufacturing ?

Additive manufacturing (AM) is a fast-growing domain that has gained a lot of attention in the last decade in the metallurgy community. Especially, powder-bed based processes like Selective Laser Melting (SLM) or Binder Jetting are of great interest for the production of high-end parts for applications with strong quality requirements (i.e. aerospace).

In powder-bed processes, a thin layer of powder is deposited on top of previous layers and then locally melted with a Laser (SLM) or impregnated with a binder (Binder Jetting). The quality of the deposited layer is of huge importance to guarantee the absence of defects that could introduce weakness in the final part.

The two main characteristics to consider are the spatial homogeneity of the layer, i.e. the absence of irregularities on the surface, and the density of the powder bed. A good evaluation of the spreadability, i.e. the ability of a powder to produce homogeneous layers, is thus an essential requirement of these processes.

Current In-Situ Method Costly and Time-Consuming: A New Procedure Needed

Predicting the spreadability of a powder is a difficult task. The lack of physical description of particle interactions is inherent to the major complexity of powders. Consequently, is it impossible to predict the spreadability by only considering particle properties (size, shape, surface chemistry). Also, the complex interaction between the recoating system and the particles makes it even more difficult. Despite promising advances being made thanks to DEM numerical simulations, providing a proper calibration process is used (read more on DEM simulations here), these methods are still at the development stage.

Therefore, users of powder-based AM processes have to perform an in-situ evaluation of the spreadability of their powders. However, this is a very costly and time-consuming procedure. Indeed, filling up the machine requires a latch batch of powder, which induces high material costs, especially at the powder formulation stage. After the tests, the powder has to be removed from the machine and a subsequent cleaning has to be performed. In addition, the machine has to be put out of production during the tests, leading to additional costs and production schedule shifts. The development of a new procedure to assess the spreadability thus appears to be essential to support the fast growth of powder bed-based AM processes.

A Fast and Simple Methodology to Predict Powder Spreadability through Powder Flow Charaterization

During the last decade, Granutools and his collaborators have combined their efforts to develop a fast and simple methodology to predict powder spreadability through powder flow charaterization.

The initial question was: “Can we predict powder spreadability with the Cohesive Index (CI), a simple metric of the GranuDrum instrument that allows quantifying powder cohesiveness?” (see paper here).

In a first paper published in 2015 (see here), the correlation between CI and spreadability measured in a test bench has been demonstrated. The basic principle of the method is as follows. First, the maximum cohesive index value for which an acceptable spreadability is obtained in the printer is determined. Then, a quick measurement in the GranuDrum allows for determining if the powder will produce homogeneous enough layers in the printer. No longer a need to produce large batches and no need to stop the production of the machine for the spreadability evaluation. The initial study has then been extended by including in-situ evaluation in an SLM printer. Indeed, the quality of the spread layers is also influenced by the recoater characteristics (wiper material, geometry, translational speed). The results obtained were used as a basis for the publication of a new ISO/ASTM standard.

Conclusion: A Good and Efficient Evaluation of Spreadability is Essential for Improving Quality in Powder-Bed Based AM

In this blog, we have seen that a good and efficient evaluation of spreadability is essential for improving quality in powder-bed based AM. The methodology, using powder flow characterization, allows fast and easy prediction of powder spreadability without the costly and time-consuming evaluation in the machine.

We are continuing this work with our academic collaborators, so new results should arrive soon!

If you are interested in this topic don’t hesitate to keep in touch with us!

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