MRI: Development of an Additive Rapid Prototyping Instrument for Advanced Manufacturing Research
Additive manufacturing (AM)-- the process of making a three-dimensional object from a digital computer model-- has the potential to revolutionize the way things are made. It enables design-driven and personalized manufacturing and the production of complex parts and structures. This rapidly evolving technology is being used by companies in industries ranging from healthcare to national defense but with current AM processes there are limitations in the accuracy and quality of the parts that can be made. This Major Research Instrumentation award will support the development of an integrated Additive Rapid Prototyping Instrument with multiple AM operations and real-time advanced sensing and control functions. This will enable fundamental research on the fabrication of products from different materials with intricate features and enhanced mechanical properties. This award advances the frontiers of AM and fundamental multidisciplinary research and education and promotes technology transfer to enhance the competitiveness of the US manufacturing sector.
The significant feature of this instrument will be its ability to realize multi-physics, multi-material and multi-scale processes for improved accuracy, surface finish, and properties. This multi-functional modular system will perform laser-engineered net or powder bed fusion, with subsystems for accuracy and property enhancements, and process monitoring, operating in a common command-and-control environment. The instrument will integrate several processing energy sources with computer numerically-controlled precision and high-speed high-sensitivity instrumentation. The dominant scientific and technological challenges to be addressed are the realization of the integrated system and the understanding and modeling of hybrid processes and process chains in such an environment. Incorporation of sensors and instrumentation for in situ real-time measurements of relevant physical process parameters as a function of time and position will be crucial to the understanding of interactions that take place between processing energy sources and the workpiece. This fundamental knowledge provides a key stepping stone in advancing the frontiers of additive manufacturing.
