NSF Award
2435524
The broader impact of this I-Corps project is the development of automated, 3D, hybrid, metal printing software and its associated instrumentation. This technology promises to increase production speeds and reduce costs, making 3D metal printing more accessible and efficient. The technology ensures high-quality printed parts with a reproducible process, targeting demanding applications in the automotive and aerospace industries. Moreover, the process versatility extends to plastic printing and other domains, facilitating rapid prototyping and product development cycles. The innovation promises substantial economic benefits by lowering the cost per part for low-volume production and minimizing slow-moving inventory. The technology aims to optimize material usage and energy consumption, contributing to environmental sustainability, and reducing overall carbon dioxide (CO2) emissions. Ultimately, this project serves as a critical step for transitioning additive manufacturing from prototyping to high-volume production, offering transformative opportunities across various industrial sectors.<br/><br/>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. This solution is based on the development of a smart, automated, 3D printing technology to address challenges inherent in 3D metal additive and hybrid processes, which while flexible, often suffer from high costs and extended production times due to material inconsistencies and variable print quality. This intelligent solution harnesses the full potential of hybrid printing and expands the capabilities for printing 3D components using diverse metal and metal alloy materials. Unlike previous work, this technology automates the 3D printing process using artificial intelligence and machine learning algorithms, eliminating human interaction. It addresses the challenges by providing 3D metal printing resulting in: (1) an efficient printing process that blends machine learning and artificial intelligence to streamline printing, reducing production times and enhancing competitiveness; (2) a cost-effective process that minimizes preprocessing, machine parameter setup, and printing time, improving economic viability; and (3) reproducibility and quality assurance that ensures consistent material properties and reproducible processes through an innovative in situ, quality-controlled, process-enhancing component reliability and quality.<br/><br/>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.
