Ding research group is pioneering advancements in metal Additive Manufacturing (AM) by developing innovative processes that integrate thermal management, laser and ultrasonic peening, and in-situ reactions. These cutting-edge techniques aim to improve the mechanical properties and surface finish of 3D printed metal parts, opening up new possibilities for their application across various industries.

Our research in this area aims to develop an inexpensive high-throughput laser-silanization based process to fabricate micro/nanoscale structures on the metal surface to achieve multi-functionalities that combines tunable wettability, self-cleaning, antibacterial, anti-reflectivity and anti-icing. The existing technologies take a while (up to two full hours to treat a single square inch of metal) whereas this technology few tens of seconds to process the same. The main objective of our research is to understand the processing science of surface micro/nanostructure generation on the engineering metal alloys. Our results showed that surface chemistry of the micro/nanostructured surface is also equally critical to achieving target wettability condition. Similar micro/nanostructured surface with fluro-silane chemistry behaved as water repellent whereas with cyano-silane chemistry behaved as water attractive. 

Our research presented a new electronically tunable THz bandpass optics which is also highly transparent in the visual spectrum. Our results render an economical technique capable of treating large surface area for multi-functional metamaterials and provide a viable solution for fabrication of tunable THz lens for sensing and imaging. More details can be found in the Scientific Reports paper.  

Our research group leads the way in developing both physics-based and data-driven models to enhance manufacturing processes and materials science. We specialize in creating thermo-mechanical-metallurgical coupled models for machining, forming, surface treatment, and ultrasonic welding, along with predicting microstructural evolution. Our machine learning models harness data to optimize process parameters, enabling smarter and more efficient manufacturing practices.

We are advancing the field of machining by integrating laser technology, enhancing cutting performance, reducing tool wear, and achieving superior surface finishes. This approach enables efficient machining of challenging materials and complex geometries.

Who We Work With

  • National Science Foundation
  • Naval Research Laboratory
  • US Army
  • Oak Ridge National Laboratory
  • Pacific Northwest National Laboratory
  • IPG Photonics
  • General Motors
  • GF Machining Solutions
  • Iowa State University
  • Purdue University
  • Penn State University
  • Rensselaer Polytechnic Institute