I am deeply passionate about additive manufacturing, with hands-on experience in both polymer and metal 3D printing processes. My expertise lies primarily in 3D printing, where I have worked extensively on optimizing manufacturing techniques and computational modeling. During my time at UNC Charlotte, I also had the opportunity to work on welding computational modeling, further expanding my knowledge of material behavior in advanced manufacturing processes. For my master’s thesis, I conducted computational modeling of the metal 3D printing process, focusing on its first and most critical step—powder spreading. This research allowed me to analyze and optimize material distribution, ensuring a strong foundation for the subsequent printing stages.

Computational Modeling of Powder Spreading in Selective Laser Sintering (SLS)

Powder spreading is the first and most critical step in Selective Laser Sintering (SLS), a metal additive manufacturing process. This process involves using a blade or spreader to evenly distribute powder across the build platform. After each layer is spread, a high-powered laser selectively fuses the powder to form the desired geometry. The process is repeated layer by layer until the final component is produced. The quality and consistency of the powder bed directly impact the final part's surface finish, mechanical properties, and overall precision.

Master’s Thesis Research

For my master’s thesis, I developed a computational model to simulate the powder spreading process in SLS, providing a strong foundation for ongoing research in my department. My work utilized the Discrete Element Method (DEM), implemented in Altair’s EDEM software, to accurately simulate powder behavior and spreading dynamics. Developing this computational model required over a year of in-depth research into DEM simulations and SLS processing techniques.

Research Outcomes

Through an extensive parametric study, I identified key process parameters that influence powder bed quality and surface finish in SLS. This research:

  • Optimized critical parameters to improve the uniformity of the powder layer
  • Reduced machine time and operating costs, saving thousands of dollars in experimental testing
  • Provided a validated computational framework aligned with published experimental results worldwide

My work in computational modeling demonstrated the potential for simulation-driven process optimization in metal additive manufacturing, contributing valuable insights to improve part quality, reduce defects, and enhance overall efficiency in SLS-based production.

The copy of my thesis and the paper I published can be found the links below:

  1. Thesis: Discrete Element Simulations of Powder Spreading in Additive Manufacturing
  2. Discrete Element Modeling of Scraping Process and Quantification of Powder Bed Quality for SLM. Conference paper published in MSEC: ASME 2020 15th International Manufacturing Science and Engineering Conference