Instead of requiring surgery to insert a solid scaffold, the gel could be simply injected into the knee, a much less invasive procedure
A lifetime of activity can gradually erode the cartilage that cushions our joints. Someday, we might simply inject a gel to repair it, University of Connecticut researchers report in the Oct. 6 issue of Nature Communications.
Read more by following the link below:
Continue reading
By Claire Tremont, Manager of Communications and Digital Strategy
Operated through the University of Connecticut School of Engineering, the Digital Design Research, Analysis, and Manufacturing (D2REAM) Center – an academic-government-industry partnership that will develop groundbreaking modeling and simulation capabilities for the next generation of Army ground vehicle systems – aims to support advanced structural digital design and manufacturing, and discovery of novel metamaterials.
By using the strong research ecosystems at UConn, the center, which launched in July, looks to build a stronger partnership between academia, government, and industry. The center is supported by a $4 million round of funding in its first year, and by an additional $5 million in the second year.
“Our objective is to formulate and develop novel digital engineering models that will help the Army make better predictions, which in turn will further reduce the need to build physical prototypes,” says Mechanical Engineering Professor and Department Head Horea Ilies, who leads the center along with Castleman Professor of Engineering Innovation Associate Professor Julián Norato. “We have at UConn one of the strongest computational design and manufacturing groups in the nation.”
Read more on the UConn Today
and visit https://dream.engineering.uconn.edu/.
by Joanna Giano, Written Communications Assistant
UConn’s team of Mechanical Engineering Graduate students achieved a remarkable victory, securing first place at the national hackathon event hosted by The Computer & Information in Engineering (CIE) Division of the American Society of Mechanical Engineers (ASME). This annual competition featured 34 participants from 18 institutions and took place from August 20 to 23, 2023, at the Boston Park Plaza in Boston, MA.
The dynamic duo of Leidong Xu and Zihan Wang, both PhD students affiliated with Prof. Hongyi Xu’s Computation Design for Manufacturing Laboratory, earned the grand prize of $1,400 for their outstanding performance. The second-place team received $700, while the third-place team received $350.
The ASME-hosted hackathon presented an invaluable opportunity for participants to immerse themselves in the practical applications of data science and machine learning techniques to solve real-world engineering challenges. The primary objective of this competition was to develop realistic textures for solid objects created using computer-aided design (CAD) software. These textures were expected to mimic the behavior of real-world materials like metals and alloys across various scales.
UConn’s triumph at this national event is a testament to the exceptional talent and dedication of its Mechanical Engineering students, showcasing their ability to harness cutting-edge technology to address complex engineering problems. The students and Prof. Xu delved deeper into their journey leading up to and during the hackathon below.
The hackathon event has a tight timeframe, and it is a huge challenge for us to develop a complete and polish project. To overcome it, we allocate time wisely and finally get all results done in one week.
Zihan and Leidong enhanced an existing system that utilized 2D microstructure images to recreate 3D microstructures that are statistically equivalent. Our advanced framework employs a Transfer Learning model to capture essential features from the granular microstructures of alloys. Notably, we’ve augmented computational efficiency through
parallel computing, which also allows our generated microstructures to be incorporated into intricate 3D volumes like tubes, helical gears, and turbo blades. Our methodology integrates transfer learning via VGG-19, style transfer techniques for texture synthesis, and a multi-GPU parallel approach. Beyond its technical prowess, our framework addresses a crucial design hurdle, bridging the gap between microstructures and designers’ vision seamlessly.
With the rapid evolution of machine learning methodologies in recent times, it’s imperative for researchers to first understand the inherent characteristics of their data before selecting an approach. From there, adapting and tweaking existing frameworks or strategies can be pivotal in optimizing results.
We are very familiar with the programming and visualization tools we used during the hackathon. Additionally, we possess sufficient expertise in pre-trained deep learning models, image-processing methods, and style transfer techniques. This proficiency greatly expedited our problem-solving process throughout the hackathon.
With the limited time at hand, we realized that we needed to capitalize on the advantage of using pre-trained deep learning models to tackle the challenge effectively. Initially, we had planned to build our solution from the ground up, training our own models and optimizing them for the specific problem we were addressing. However, given the tight timeframe, this approach would have consumed a substantial portion of our available time. Upon evaluating our situation, we recognized that leveraging pre-trained models could provide us with a significant head start. These models were already trained on vast amounts of data and had learned complex patterns, making them well-suited for our problem as well. This shift in strategy allowed us to save precious time on training and focus more on adapting the model to our specific needs.
Our victory at the ASME 2023 CIE Hackathon has reinforced our drive to further refine and innovate our current framework. We see a multitude of avenues for enhancement. Specifically, we’re eager to develop a fully automated system for image analysis and labeling, which would drastically streamline the process. Another focus is to fine-tune our parallel algorithm to produce microstructure images with even greater resolution. Moreover, in a bid to consolidate our findings and methods, we’re excited about our upcoming collaboration with Sandia National Laboratories. Our joint effort aims to encapsulate our hackathon project into a comprehensive journal paper, sharing our innovations with the broader scientific community.
IDETC/CIE hackathon is an opportunity to engage with real-world engineering problems, moving beyond academic theory. This setting will allow me to apply my technical knowledge in a practical context, enhancing my understanding of the mission and challenges of national labs and leading industry companies. Participating in the hackathon in a team will serve as an excellent opportunity for honing my teamwork and cooperative abilities. The exchange of ideas, innovation, and sense of camaraderie within such events play a critical role in my future career.
We started on our computational research journey during our undergraduate years. For us, coding transcends mere functionality; it is an art form. We firmly believe that the elegance and precision of the code play a pivotal role in determining the quality of the final research output. Consequently, we always strive to craft our code with extra care and refinement, ensuring that it not only fulfills its intended purpose but also stands as a testament to our dedication and passion.
We heavily relied on the PyTorch package, which is based on the Python programming language, to implement our innovative idea. Beyond that, deep learning methods and image analysis techniques are both very important to contribute to our success.
To ensure sustained progress in our coding capabilities and awareness of cutting-edge developments, we’ve mapped out a multi-faceted approach. This includes actively participating in tech competitions, which challenges our problem-solving abilities and exposes us to diverse perspectives. Furthermore, attending conferences allows us to gain firsthand insights from industry leaders and pioneers. Finally, keeping abreast of the latest
A timed-release patch made of biodegradable polymer by a team led by Prof. Thanh Duc Nguyen could make antibody treatments more accessible and safer.
Read more by following the link below:
Prof. Wilson Chiu is a new member of the 2022 Class of the Electrochemical Society Fellows.
For more information, read the ECS announcement by following the that follows below.
A new paper published in Applied Physics Letters co-authored by Prof. Osama Bilal and his PhD student introduces materials that can simultaneously block sound and vibrations at tunable frequencies by design.
By studying rabbits, Prof. Thanh Nguyen‘s group is using biodegradable piezoelectric film to aid in growth of new cartilage.
Read the whole science highlight on the NIH’s website here:
https://www.nibib.nih.gov/news-events/newsroom/recharging-cartilage-after-knee-damage
Two ME professors received the 2020 National Science Foundation’s CAREER award, which is the Foundation’s most prestigious award in support of early-career faculty.
Prof. Xinyu Zhao’s 500k CAREER award focuses on developing a fundamental understanding of flame extinction, which plays a central role in promoting energy security, environmental sustainability, air-travel safety and opportune fire suppression. Droplets, such as fuel sprays in aeronautical combustors and water droplets in pollutant reduction or fire suppression, are ubiquitous in practical combustion systems. When interacting with an established gaseous flame, droplets introduce additional mechanisms to extinguish a flame, through physical processes such as vaporization, dilution, subsequent reactions, modulation of turbulence, and radiative heat transfer. Through this project, Prof. Zhao will investigate the fundamental understanding and develop quantitative descriptions of key factors governing the flame extinction process in presence of droplets.
The 592k CAREER award received by Prof. Ying Li will support fundamental research to understand complex mechanical behaviors of thermoplastic elastomers (TPEs). Biodegradable TPEs have the great potential to be used as protective coatings for cell phones, artificial muscles for soft robotics, and polymer electrolytes for batteries. This research project aims to understand and quantify the link between synthesis, microstructure, and mechanical property of TPEs, with the help of multi-scale computational modeling, machine learning, and experimental validation. With tailored mechanical properties, these biodegradable and environment-friendly TPEs can be widely used further to enable an array of novel structural and device applications, alleviating the plastic pollution crisis.