Researchers from UNT Engineering will collaborate with the Center for Nanophase Materials Sciences at Oak Ridge National Laboratory, the largest U.S. Department of Energy science and energy laboratory, as part of a $1.5 million grant from the DOE to advance 3D printed materials for automotive body frames.
Principal investigators, Wonbong Choi, a professor in the UNT Department of Materials Science and Engineering and the Department of Mechanical Engineering and Yijie Jiang, an assistant professor in UNT’s Department of Mechanical Engineering received more than $400,000 of the overall grant to develop the fabrication of a lightweight, 3D printed carbon fiber composite material equipped with embedded sensors for use in car structures.
Choi and Jiang will evaluate various materials using sensors embedded into the material during printing. These flexible micro sensors will be located precisely during 3D printing and will deliver real-time performance data during the material’s testing phase.
Once the material is successfully tested and installed in cars, embedded sensor technology will provide dashboard data for car owners. Similar to current sensor technology that lets drivers know when their fuel or tire pressure is low, the embedded sensors will identify and provide alerts for issues in the car structure.
Car frames are currently mass produced using subtractive manufacturing where specified shapes are cut from a sheet or slab of material and then molded into parts. Once the shapes are cut, the remaining material is discarded or must be recycled for reuse creating waste and additional cost.
Additive manufacturing eliminates waste and production cost. Rather than cut away materials, additive manufacturing uses data computer-aided-design (CAD) software or 3D object scanners to deposit material, layer upon layer, in precise shapes and intricate designs that are often too small or awkward to create by cutting away material.
Although 3D printing technology has been around for decades and can improve performance and produce complex structures with simplified fabrication, the lack of readily available materials and affordable mass production options make it less cost-effective than current subtractive manufacturing.
“Once we have developed and successfully tested materials, we can provide stronger, safer and more lightweight options for automotive additive manufacturing,” said Jiang. “Readily available, proven materials will provide opportunities to lower costs and advance the industry.”
Choi and Jiang will conduct their research over the next three years in the UNT Center for Agile and Adaptive Additive Manufacturing, one of the most advanced university research facilities in the nation for materials analysis. CAAAM was launched in 2019 with a $10 million appropriation by the State of Texas Legislature.
“Prestigious labs from all over the country submitted proposals to participate in this DOE collaboration with Oak Ridge National Laboratory,” Choi said. “Our selection is a testament to the innovative research and industry collaboration CAAAM provides. Through this grant we will advance materials and technology available to industry, offer students hands-on experience in the field and elevate UNT’s status as a Tier One research university.
written by Kris Muller
Title: 3D printed multifunctional structure in advanced vehicle with an embedded sensor for in-situ vehicle health monitoring
Program: DOE Lab Call Projects
Partnering with the Oak Ridge National lab and total award is ~$1.5M(UNT portion : $405,000, PI- wonbong Choi, Co-PI: Yijie jiang)
Duration: 3 years starting from October 1, 2020
University of North Texas (UNT) and collaborators in Oak Ridge National Laboratory (ORNL, the PI institution) will pursue research on materials and fabrication of advanced polymer/carbon fiber (CF) composites for light-weighting and sensing multi-functionalities.
The specific proposed tasks for UNT are listed below: 1. enhanced 3d printed organic-inorganic interface for long-term performance and, 2.real time evaluation of material properties with embedded sensors.