3D Printing

Pratt & Whitney Additive Manufacturing Center Expands Defense Research

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image of jet and submarineThe Pratt & Whitney Additive Manufacturing Center (AMC) at UConn Tech Park has expanded its Department of Defense-related research efforts in recent months with new projects related to submarine and aerospace manufacturing.

The submarine industrial base hopes to meet the demand for quality submarine parts by focusing increasingly on additive manufacturing. A team of UConn materials science and engineering faculty along with colleagues from the University of Rhode Island recently started a four-year project funded by the National Institute for Undersea Vehicle Technology (NIUVT) to investigate properties of a steel commonly used in submarine production. The team will explore the material characteristics of parts made of this steel using additive manufacturing as compared to traditional manufacturing technologies such as castings and forgings.

The AMC supports the additive manufacturing aspects of the project that include powder characterization as well as chemical and thermal analysis besides the production of parts. In its newest NIUVT-funded project the AMC will exploit the layer-by-layer manufacturing approach of additive manufacturing to tailor the behavior of bronze materials at specific locations within a part. What is nearly impossible with castings can likely be accomplished with additive manufacturing, for example, to optimize sections of parts for high strength while other regions bear the brunt of energy absorption during service.

The NIUVT additive manufacturing projects and the AMC involvement echo parallel efforts by the Navy to develop an industrial base for additive manufacturing of submarine parts. To this end, the Navy set up an additive manufacturing Center of Excellence in 2022 and in the same context invited researchers from seven US universities to form an academic consortium.

The AMC is part of the consortium and will soon embark on its first project and address the important aspect of metal powder characteristics. Key additive manufacturing technologies use metal powder, and a detailed knowledge of the powder characteristics and flow behavior is needed to advance additive manufacturing to a production level.

Similarly, the Air Force pursues additive manufacturing for some of their current and future systems, particularly in high-temperature applications. Recently, the AMC started a new four-year project sponsored by the Air Force Research Laboratory (AFRL) on refractory metals for additive manufacturing of high-temperature components. Refractory metals such as niobium have melting points well over 4,000 degrees Fahrenheit but have been difficult to produce with conventional manufacturing technologies. The AMC will investigate process conditions during additive manufacturing and their effects on the details of the niobium metals that matter for their use in high-temperature applications.

With the NIUVT, Navy, and Air Force research activities, the AMC supports some of the most critical applications for the nation and in the process prepares students with expertise in state-of-the-art manufacturing technologies.

Leveraging Active Machine Learning to Optimize 3D Printing Autonomously

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Prof. Anson Ma demonstrates the machine learning capabilities of the HuskyJet 3D printer at the SHAP3D lab in IPB.
Prof. Anson Ma demonstrates the machine learning capabilities of the HuskyJet 3D printer at the SHAP3D lab in IPB.

Inkjet printing has evolved from a graphics and marking technology to a broader variety of additive manufacturing and 3D printing processes for electronic, optical, pharmaceutical, and biological applications. The success of adopting inkjet technology for these newer applications is contingent on whether the ink materials can be consistently and reliably jetted by the print systems. Currently, each printer-and-ink combination requires calibration by trial and error, which consumes a considerable amount of time and materials. IPB researcher, Prof. Anson Ma, Site Director of SHAP3D, teamed up with UConn machine learning expert, Prof. Qian Yang, to demonstrate a new concept of “autonomous 3D printing”, leveraging an active machine learning method they developed to efficiently create a jettability diagram that predicts the best conditions for jetting an ink from a printhead.

Briefly, a camera is used to image the printhead and capture the behavior of ink jetted from a printhead. Starting with a few randomly chosen conditions, a machine learning algorithm predicts the optimal jetting conditions and then “cleverly decides” on the next set of experiments that can further improve prediction accuracy. After performing those experiments, the algorithm analyzes the newly acquired images, updates the prediction for the desired jetting conditions, and iteratively selects the next experiments, continuing autonomously until a small experimental budget is reached. This approach has achieved a prediction accuracy of more than 95% while considerably reducing the number of experiments required by 80% compared to a typical grid-search approach. This novel approach is especially powerful for optimizing complex print systems with many tunable process parameters.

This work was recently published in the journal 3D Printing and Additive Manufacturing (http://doi.org/10.1089/3dp.2023.0023) and led to a pending patent application (WO 2023/2788542).

Manufacturers Proactive in Response to COVID-19

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The COVID-19 pandemic has affected manufacturers in every sector. In the face of disruptions to production and supply chains, disproportionate decreases in product demand and many other challenges, manufacturers are taking a close look at strategies that will help them be better prepared for future pandemics or other disasters. How will they protect their core businesses and keep their companies afloat?

Connecticut Manufacturing Resource Center (CMRC) at Tech Park is working with small- and medium-size manufacturers to develop long term solutions to this critical issue. 

Using funds from a recently awarded $300K EDA CARES Act grant, CMRC is proactively helping companies establish contingency plans for effectively maintaining operations from an off-site location during a crisis. Participating companies receive access to product lifecycle digital technologies that establish a kind of smart backup at Tech Park, a digital twin of a company’s operations that helps ensure minimal disruption.

Joe Luciani, Director of the Proof of Concept Center (POCC) at Tech Park, helps manage the grant. He believes that this vital support is coming at a crucial time, stressing that “The pandemic has severely impacted these companies and they are eager to put safeguards in place.” Hadi Bozorgmanesh, PI for the grant and Director of CMRC, adds with conviction, “Our manufacturing sector is a major source of economic strength for Connecticut and we need to help these organizations find long term solutions as they start to recover from the crisis.”

CMRC is already seeing success. Sunil Agrawal, Vice President of R&D Dynamics Corporation, a manufacturing company in Bloomfield, CT, recently completed the program. He contacted Hadi with gratitude and praise for UConn’s outstanding dedication and support throughout the project. He affirmed his organization’s commitment to implement the recommended changes, and his conviction that “The result will be a company that is not only more efficient and capable, but more resilient in a crisis like the one brought on by the events of the last twelve months.”

UConn is seeking additional partners who will receive funding support in a cost-share arrangement. Contact Hadi Bozorgmanesh, hadi.bozorgmanesh@uconn.edu.

The Enterprise Solution Center (ESC) at UConn Tech Park comprises four research centers: Quiet Corner Innovation Cluster (QCIC), Proof of Concept Center (POCC), Connecticut Manufacturing Simulation Center (CMSC), and Connecticut Manufacturing Resource Center (CMRC).

ESC takes an integrated approach to co-development of technology products and services to support the competitiveness of small and medium manufacturers.

3D Printing the Extracellular Matrix

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Anna Tarakanova, Assistant Professor in Mechanical Engineering and Biomedical Engineering at UConn, studies ways that the structural features of complex networks like extracellular matrices (ECM) support their function. To explore this further, she decided to find a way to recreate her ECM micro-structure data into enlarged physical models that are visible to the naked eye.

photo of 3D print
ECM printed with the Stratasys Design F370

She approached Joe Luciani, Director of the Proof of Concept Center (POCC), an experienced innovator at Tech Park. His extensive collaborative experience with faculty, students and companies of all sizes paired with his expertise utilizing POCC’s state-of-the-art prototyping and fabrication equipment was well aligned with Anna’s research goals.

To kick off the project, Anna provided her structural data set as a shareable source. Joe converted the file using 3D modeling software, Rhinoceros3D, with a visual programming plugin, Grasshopper3D, that crunched the data to produce a network of nodes and linkages – i.e., a 3D model. Joe used this 3D model to successfully print larger-than-life ECM samples in POCC’s Stratasys 3D printers. He also recorded the printing process by programming one of the center’s robots to hold a camera and move through a few waypoints.

Anna provides some additional insight into her research. She explains, “Our research is focused on understanding the structure-function connectivity of complex heterogeneous systems like extracellular matrix (ECM) networks [the printed specimen] by establishing a microstructure-resolved deep learning framework that couples imaging data of matrix microstructure with multiscale computational modeling from the nanoscale, 3D printed prototyping, and in vitro mechanical testing of tissue specimens.”

The project is a new collaboration between Professor Tarakanova, Hongyi Xu, Assistant Professor in Mechanical Engineering at UConn, and David M. Pierce, Associate Professor in Mechanical Engineering and Biomedical Engineering at UConn. 

“This is an exciting new line of convergent research that utilizes heterogeneous 2D experimental microscopic imaging data to stochastically reconstruct realistic 3D matrix structures through a statistically-driven, generative deep learning approach for a representative model system: healthy and osteoarthritic articular cartilage, ” says Pierce.