Method of Determining the Microstructure of Additively Manufactured Metallic Materials using In-Situ Process Monitoring Data

Case ID:

The technology is a method that can improve additive manufacturing by actively monitoring the deposition of materials. In this novel method, the in-situ monitoring data plays a critical role in enhancing the understanding and control of microstructural development during the printing process. By leveraging this data, the technology can dynamically adjust the additive manufacturing parameters in real-time to optimize the microstructure of the final product. This allows for greater precision in achieving desired mechanical properties and reduces the risk of defects while increasing production efficiency and the reliability of manufactured parts. Furthermore, this approach opens up possibilities for creating complex, high-performance materials that are tailored to specific applications, setting a new standard in the field of additive manufacturing.

Additive manufacturing techniques like laser powder bed fusion create material through the successive melting of layers in a powder feedstock. The resulting microstructures in nickel superalloys differ significantly from those produced through conventional cast or wrought processes, showcasing characteristics like cellular structures and epitaxial grain growth. The specific attributes of these microstructures are influenced by factors such as local thermal history, alloy composition, and processing parameters.

In-situ monitoring data from additive manufacturing processes can provide information on the local thermal conditions experienced by a metal during the printing process. If processed correctly, the extracted thermal history information can be utilized in materials models to predict the microstructures of the material throughout different locations in a component. This has the potential to be used for in-process quality control of additively manufactured parts. 


  • Additive manufacturing 
  • Metal additive manufacturing
  • Microstructure prediction


  • Allows for better process design and optimized microstructure in the final product
  • Helps mitigate microscopic defects such as porosity and cracks
  • Mitigate undesirable phases of non-optimal grain structures
  • Dynamic, real-time adjustment of manufacturing parameters
  • Increased efficiency
Patent Information:
Contact For More Information:
Tariq Ahmed
Sr Licensing Manager, College of Engineering
The University of Arizona
Lead Inventor(s):
Andrew Wessman
Mohammed Shafae