Predicting Powder Spreadability in Metal AM: Combining Powder Characterization and DEM Simulations

By Aurélien Neveu – Granutools

Ensuring homogeneous and stable powder layers remains one of the critical challenges in Powder Bed Fusion (PBF) additive manufacturing. Thin layers often below 100 μm must be evenly deposited to guarantee melt‑pool stability, repeatability, and mechanical integrity of the final part. In this study, Aurélien Neveu (Granutools) extends earlier work linking powder cohesiveness to spreadability, combining advanced laboratory characterization with in‑printer imaging and Discrete Element Method (DEM) simulations.

🔬 Understanding the Link Between Cohesion and Spreadability

Powder cohesiveness directly affects its ability to flow and form uniform layers. Previous studies have shown that powders with high cohesive forces tend to form agglomerates, causing:

• Layer irregularities
• Localized defects
• Poor laser interaction
• Increased porosity in final parts

Using GranuDrum, the Cohesive Index (CI) quantifies cohesiveness across different rotational speeds. In this extended study, nine widely used AM powders, Ti alloys, AlSi12, 316L, FeCo were analyzed, covering a broad particle‑size spectrum (10 µm to 105 µm).

The results confirm a robust trend:
Higher CI → higher interface fluctuation → poorer spreadability.

This strong correlation, validated on a wider set of powders, supports the CI as a reliable predictive metric for AM powder performance.

📷 In‑Printer Layer Analysis (SLM®280)

To evaluate real print behavior, 15 consecutive layers were deposited inside an SLM®280 system. A built‑in camera captured grayscale images after each recoater pass. An interface‑fluctuation metric aligned with ISO/ASTM TR 52952:2023 was used to quantify the homogeneity of each layer.

This method enabled:

• Detection of subtle layer defects
• Comparison between powders
• Validation of the CI-to-spreadability correlation
• Identification of outlier cases needing refined image processing

AlSi12, the most cohesive powder, showed the highest interface fluctuation, confirming its low spreadability.

🖥 DEM Simulations: Understanding Powder Behavior at Particle Scale

To complement experimental data, DEM simulations were performed using digital twins of the real spreading conditions.

Key insights include:

• Increasing cohesive energy density (CED) increases layer roughness
• Cohesion is the dominant driver of layer irregularities
• Rolling friction exhibits minimal influence
• Simulated layer patterns mirror experimental defects

DEM gives access to particle-level measurements impossible to obtain experimentally, enabling deeper understanding of defect formation mechanisms.

🟦 Conclusion

This combined experimental numerical study confirms that powder cohesiveness is a key predictor of spreadability, regardless of material family. The integration of:

• GranuDrum Cohesive Index
• SLM280 in‑printer imaging (IF metric)
• DEM simulations

provides a highly reliable workflow for early powder evaluation and optimization.

For AM users and powder suppliers, this approach enables:

• Faster powder qualification
• Better control of layer quality
• Improved printing reproducibility
• Reduced trial‑and‑error during process setup

This work by Aurélien Neveu reinforces the role of advanced powder characterization in enabling the next generation of metal AM performance.