Exploring the value of particle characterization for additive manufacturing and the best tools for the job

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Additive manufacturing (AM), commonly known as 3D printing, is fast taking off as an efficient, sustainable, and cost-effective way to produce a wide range of components with unique performance characteristics. AM can also remove the need for complex shipping arrangements, relying instead on digital files to print products on-demand. From aerospace to automotive engineering, and from the medical to the dental industry, this evolving technology is revolutionizing industries across the world.

One of the fastest growing AM technologies is metal powder bed fusion (metal-PBF) which can produce complex geometries by melting metal powder particles layer-by-layer. To get a flawless part you need to ensure that the powder bed is consistent across each powder layer, and from build-to-build. The best way to validate this is to check the particle size and particle shape of your powder feedstock, since these properties affect powder packing, flowability, response to heat, and surface finish, which are all critical to this AM process.

In this webinar Dr Jenny Burt and Dr Ben Lynch will discuss the importance of particle size and shape in the metal-PBF process. They will show how laser diffraction (Mastersizer 3000+) and static image analysis (Morphologi 4) can be used to accurately measure these characteristics, and even predict process performance.

演讲嘉宾

  • Jenny Burt - Senior Applications Specialist - Physico-Chem, Malvern Panalytical
  • Benjamin Lynch - Applications Specialist, Malvern Panalytical

更多信息

Who should attend? 

  • Anyone working with powders for additive manufacturing. The focus will be on metal powders, but content is applicable to polymer and ceramic powders also.

What will you learn? 

  • Understand why particle size and shape are critical for the powder bed fusion process.
  • Learn about Malvern Panalytical’s techniques for particle size and shape analysis.
  • Learn how to easily interpret and classify data to predict powder quality and performance.