Skip to content

Computational Fluid Dynamics for Process Control and Optimization in Additive Manufacturing

  • today
  • access_time 11:00 - 11:25 AM EDT
  • location_onRoom 140F
  • blur_circularConference
  • monetization_onPaid Upgrade
  • schoolOptimization

Process control and optimization in additive manufacturing (AM) pose significant challenges that are difficult to address with experiments alone. High fidelity computational fluid dynamics (CFD) simulations coupled with laser-material interactions are providing key insights into processing techniques that mitigate defects such as porosity, lack of fusion, and spatter. This presentation will cover case studies from AM researchers using CFD to study the effects of beam shaping on melt-pool dynamics and associated defect mitigation, printing on uneven and unsupported surfaces and subsequent microstructure and thermal stress evolution. Multi-core beams have gained interest in recent years as a viable method for increasing production speeds and improving part quality due to the more even heat flux distribution. CFD simulations performed by Flow Science in collaboration with industry partners have revealed that using multi-core beams, as opposed to traditional Gaussian beams, results in a more quiescent melt-pool with lower average molten velocities. In addition to increased production efficiency, this directly affects the solidification and cooling rates, which can have a positive impact on microstructure and thermal stress evolution. Another challenging area for AM processing is printing on overhang surfaces where powder is more easily overheated, resulting in keyhole melting, or dross. Researchers at the Technical University of Denmark performed CFD simulations based on experiments to characterize the driving force behind dross formation and identify energy densities that achieve sufficient fusion without dross formation or porosity. These case studies will be presented along with the fundamental principles of CFD simulations for laser-based AM modeling. 

Learning Objectives:

  • Understand the role of computational modeling for AM process parameter development.
  • List the benefits of laser beam shaping on melt pool dynamics.
  • Describe how computational fluid dynamics simulations inform subsequent models such as microstructure and thermal stress analysis for more accurate results.