The University of North Florida (UNF) has recently secured a prestigious award from the National Science Foundation (NSF) to advance the capabilities of metal 3D printing, promising to revolutionize manufacturing reliability and sustainability. This groundbreaking project is spearheaded by Dr. Longfei Zhou, an assistant professor specializing in advanced manufacturing engineering. The award supports a team of dedicated student researchers who will develop innovative technological solutions to address persistent defects in metal additive manufacturing processes.
At the heart of the initiative lies the challenge of improving the laser powder bed fusion (LPBF) technique—a dominant method for creating complex metal components used extensively in aerospace, medical implants, and energy systems. LPBF’s precision is often compromised by microscopic powder disturbances caused by the machine’s spreading arm. These disturbances create minute streaks in the metal powder bed, resulting in flaws that can compromise the structural integrity of the final product, forcing costly reprints or total scrapping of parts.
The UNF team’s research aims to embed an intelligent, real-time quality control system within metal 3D printers. This system will continuously monitor each printing layer and detect anomalies as they happen, enabling immediate, automated corrections to mitigate the defects. By harnessing advanced sensors, machine learning algorithms, and digital twin technology, the project seeks to elevate additive manufacturing to a new standard of precision and efficiency, significantly minimizing material waste and energy consumption.
This endeavor promises substantial economic and environmental benefits by reducing the frequency of failed builds, thus lowering production costs and decreasing the environmental footprint associated with metal manufacturing. Beyond immediate industrial applications, the work is poised to strengthen the United States’ manufacturing base by enhancing production yields and facilitating sustainable manufacturing practices.
The project also emphasizes educational impact. It includes the development of new course modules and laboratory activities designed to equip students with skills at the forefront of data-driven automation in manufacturing. By integrating cutting-edge research into the curriculum, UNF aims to prepare the next generation of engineers to thrive in evolving industrial landscapes driven by automation and artificial intelligence.
In a commitment to open science and collaborative advancement, the research team plans to release publicly accessible datasets, trained machine learning models, and decision-making software platforms. The availability of these resources is expected to catalyze broader adoption across both industry and academia, accelerating innovation in metal additive manufacturing worldwide.
The student team members involved in the project are senior students in the advanced manufacturing engineering program: Maria Fernanda Ocrospoma Figueroa, Tessa Baur, and Taylor Uhruh. Notably, Baur and Ocrospoma hold leadership positions in the Society for the Advancement of Material and Process Engineering (SAMPE) Club at UNF—as president and vice president respectively—reflecting their active roles in fostering community and innovation in materials engineering.
Adding to their accolades, Maria Fernanda Ocrospoma Figueroa and Tessa Baur recently excelled in the global Additive Manufacturing Competition at SAMPE 2026 in Seattle, achieving first and second place in category B, respectively. Their achievements underscore the high caliber of talent driving this initiative.
The implications of this research extend beyond academic excellence and into the heart of industrial practice. Metal additive manufacturing stands at the frontier of producing highly complex, customized components that traditional manufacturing methods struggle to fabricate efficiently. By introducing a rapid, adaptive fault correction system into the LPBF process, the research addresses a critical bottleneck that has limited broader industrial uptake.
Furthermore, the advancements envisioned by the UNF team carry significant promise for sectors where component precision and reliability are paramount. Aerospace, for example, demands flawless parts to ensure safety and performance under extreme conditions. Medical implant manufacturing requires exacting standards to enhance biocompatibility and longevity. Energy systems benefit from durable, efficient parts that extend operational lifetimes and sustainability. The improved quality control system will help meet these stringent demands more consistently.
At the technical core, the project leverages the integration of sensor data and real-time analytics, allowing printers to become self-correcting systems. This is achieved through sophisticated modeling of the printing process and feedback loops that adjust laser parameters or recoating actions to prevent defect propagation. The creation of digital twins—virtual replicas of the physical printing environment—enables simulations that predict and prevent failures, setting a new paradigm in additive manufacturing quality assurance.
The University of North Florida is uniquely positioned to propel this research forward, combining expertise across engineering disciplines with strong community and industrial partnerships. With a robust student body exceeding 17,600 and a commitment to individualized faculty-student engagement, UNF exemplifies a modern research university dedicated to impactful innovation and workforce readiness.
This NSF award marks a significant milestone in the journey toward smarter, greener, and more reliable manufacturing technologies. It reflects an emerging consensus on the vital role of automation, data science, and digital twin frameworks in revolutionizing industrial production. As the project unfolds, it promises not only to elevate metal 3D printing but also to inspire new standards for adaptive manufacturing systems globally.
Subject of Research:
Development of real-time quality control systems in metal additive manufacturing using laser powder bed fusion.
Article Title:
University of North Florida Advances Smart Quality Assurance in Metal 3D Printing with NSF Award
News Publication Date:
Not provided.
Web References:
https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2553012
https://www.unf.edu
Image Credits:
University of North Florida
Keywords:
Metal Additive Manufacturing, Laser Powder Bed Fusion, Real-time Quality Control, Digital Twin, Advanced Manufacturing Engineering, National Science Foundation Award, 3D Printing Defect Mitigation, Sustainable Manufacturing, Automation in Manufacturing, Material Engineering Education
Tags: advanced manufacturing engineering researchaerospace metal components printingautomated defect correction 3D printinginnovative metal printing technologieslaser powder bed fusion defectsmachine learning in 3D printingmedical implant 3D printingquality control in metal additive manufacturingreal-time monitoring metal 3D printersstudent research in additive manufacturingsustainability in metal manufacturingUNF NSF grant metal 3D printing
