Nanoparticle Additivation Effects on Laser Powder Bed Fusion of Metals and Polymers-A Theoretical Concept for an...

Area of Science:

• Materials Science
• Additive Manufacturing
• Nanotechnology

Background:

• Laser powder bed fusion (PBF-LB) utilizes diverse powder compositions, including novel nanoparticle (NP) additivated feedstocks.
• NP additivation influences powder flowability, absorbance, microstructure, and final part properties in PBF-LB.
• Existing inter-laboratory studies (ILSs) on PBF-LB often overlook nano-additivated materials and standardized powder characterization.

Purpose of the Study:

• To design a large-scale ILS for quantifying the effects of NP additivation in metal and polymer PBF-LB.
• To establish standardized methods for assessing NP-additivated powder feedstock properties and resulting part characteristics.
• To develop a research data management (RDM) plan adhering to FAIR principles for additive manufacturing data.

Main Methods:

• Design of a comprehensive ILS protocol for NP-additivated PBF-LB.
• Definition of test methods for powder feedstock properties, process behavior, microstructure, and part properties.
• Implementation of a RDM plan focusing on data repeatability, reproducibility, and FAIR principles.
• Utilization of principal component analysis (PCA) for correlating material-process-microstructure-part properties.

Main Results:

• The proposed ILS design facilitates the quantification of NP additivation effects across the PBF-LB process chain.
• Standardized assessment methods will yield reliable data on repeatability and reproducibility for nano-additivated feedstocks.
• The RDM plan ensures data findability, accessibility, interoperability, and reusability, enhancing scientific discovery.

Conclusions:

• The proposed ILS and RDM plan are crucial for advancing the understanding and application of NP-additivated powders in PBF-LB.
• Standardized characterization and data management will accelerate the adoption of enhanced materials in additive manufacturing.
• This framework will enable robust correlation analysis between material properties, process parameters, microstructure, and final part performance.