I-Corps: Translation potential of accelerating process development for additive manufacturing of metals

Award ID: 2414647

The broader impact of this I-Corps project is the development of machine learning-based in-situ methods to monitor the metal additive manufacturing process. Currently, printing in metal alloys is difficult due to the complex processing nature of the materials and the associated physical phenomena. The variability in printing outcomes and the quality of printed parts is a major obstacle that reduces the quality of printed parts and the potential for their full production. This technology uses in-situ measurement, which allows for defect monitoring and quality assurance reporting, automatic feedback control, process parameter mapping, and an understanding of defect formation mechanisms. In addition, the solution studies processingstructure-property relationships, investigates the printability of new alloys, reduces the need for ex-situ characterization, and reduces material waste and production time by early identification of part failure. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. The solution is based on the development of an articial intelligence-assisted approach to aid process development and the control of the quality of parts additively manufactured by laser powder bed fusion. The method uses imagining sensors and machine learning to monitor the process in real time and to inform the optimal parameters for processing as well as the process variables that can lead to printing defects. The method does not require special, complicated setups for temperature measurements that may not work reliably for different metal alloys. The method was found to be generalizable to different types of metal alloys tested on a laser powder bed machine. The ability to control the quality of printed parts may help in adopting 3D printing of metals in a wider scope of applications. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Program Director: Ruth Shuman