| Oct 21, 2023 |
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(Nanowerk News) When Carnegie Mellon’s Manufacturing Futures Institute (MFI) issues its annual call for proposals each fall, it makes clear its objectives for offering seed funding to groups of CMU faculty whose research is aligned with MFI’s mission to inspire, engineer, and lead technological and workforce advances for agile, intelligent, efficient, resilient, and sustainable manufacturing.
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They are interested in funding research that falls into one of MFIs strategic priority areas; plays to CMU’s strengths in fields, such as AI and machine learning, coupled with advanced manufacturing technologies, such as robotics and additive manufacturing; benefits from convergent research and expertise from different disciplines; and shows potential for seeding the future of advanced manufacturing with new ideas that fuel innovation and attract future investments.
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“We receive so many great proposals across a wide variety of advanced manufacturing topics that deciding which to fund is difficult. We focus on the ones that propose innovative interdisciplinary approaches that can truly advance the state of the art. Industrial relevance is important as well, given our interest in technology transition,” said Sandra DeVincent Wolf, executive director of the MFI.
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She and Gary Fedder, faculty director of MFI, reserve the right to ask for modifications to a proposal when timelines are too aggressive, funding amounts are not fully justified, or the project is missing a partner with a vital set of skills and expertise.
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That was the case when faculty members Phil LeDuc, Burak Ozdoganlar, and Charlie Ren requested funding last year for the next phase of their ice printing research.
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The team developed a novel approach to 3D print ice structures that are small enough to create vasculature in artificial tissue and other open features inside a fabricated part by creating sacrificial templates that later form the conduits and voids. It has proven to be a promising method. Still, it relied upon a lot of trial and error, as many automated approaches do initially, in their development to achieve the specific intended geometry.
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Creating more precise and reproducible geometries necessitates developing a feedback control approach, which would intelligently adjust parameters during printing to achieve the desired intricate geometries.
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Since LeDuc and Ozdoganlar published their findings (Advanced Science, “Freeform 3D Ice Printing (3D-ICE) at the Micro Scale”), they have received positive feedback at numerous conference presentations, as well as support that has encouraged them to explore how their ice printing method can be further developed.
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The initial biomedical engineering applications were clear and showed direct potential for use in personalized medicine. Still, the method also has the potential to revolutionize manufacturing by enabling the automated production of tiny internal 3D microchannels for many applications in various fields ranging from medicine to soft robotics.
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Micromanufacturing technology and advanced approaches are fundamental to numerous critical applications across various sectors, including scientific research, healthcare, national security, and space exploration, as well as the digital transformation in industry that is at the heart of MFI’s manufacturing mission.
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According to LeDuc and Ozdoganlar, the experimental and computational methods needed to build models to control the ice printing process are challenging. The microscale size and the fast nature of the thermal-fluidic processes and phase-changes (solid, liquid, gas) processes further exacerbate the challenges.
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“We know that if we can build a sensor feedback system that can make adjustments in real-time, it will be very powerful for these approaches.” said LeDuc.
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Wolf knew just the person who could help them develop the sensor hardware and computer vision algorithms needed to create a feedback control approach to produce the degree of control needed for printing the microscale ice structures.
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Lu Li, a project scientist at Carnegie Mellon’s Robotics Institute, had already brought his artificial intelligence and robotics expertise to several other MFI-funded projects. And according to LeDuc, Li has already made some great contributions to this new project.
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With Li’s help, the team has reduced the time required for image segmentation from two to three seconds to 50 microseconds. Image segmentation is a computer vision task in machine learning that involves dividing an image into multiple segments or regions based on certain criteria so that the image can be changed into something that is more meaningful and easier to analyze.
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“This is so typical of how we work at CMU. First of all, the ability to collaborate with someone like Lu Li, who has the skills we needed to move this forward, has been fantastic. And even though the MFI funding is not a huge amount of money, it has allowed us to keep moving forward with an idea that has huge potential,” said LeDuc.
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Wolf is equally excited to be working with LeDuc, who she says was open to her suggestions, grateful for her recommendation, and brings enthusiasm and energy to our manufacturing community at CMU.
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The MFI Request for 2024 proposals is open until Friday, October 27, 2023. More information can be found on MFI’s Fellowships and Funding web page.
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