LSU mechanical engineering professor Guoqiang Li
Red Stick, the – The paper from LSU Mechanical Engineering Professor Guoqiang Li, which introduces a new material model for form memory polymers (SMPS), was published in the prestigious Journal of the Mechanics and Physics of solids. In addition, he is co-author of a book that is to be published next month, entitled “Artificial Intelligence Assisted Structural Optimization”.
Li's Paper is made by Alireza Ostadrahimi, a Ph.D. Research assistant in Li laboratory; Siavash Sarrafan, former Ph.D. Li student who graduated in 2023; and employee Alireza Enferadi and Mostafa Baghani.
In the work, the work of the research group from Li is described for the development of a model, which predicts how SMPs behave under different conditions, such as: B. large stretching or temperature changes. In addition, the possibility of driving SMPs for the design of stimuli-reacting engineering structures, from medical devices to portable technologies and from transport vehicles to sea ships.
“In healthcare, [our research] Supports more intelligent designs for self -expanding stents on adaptive braces that correspond to the body, ”said Li.“ In consumer technology, it facilitates the development of flexible, durable components for foldable screens and portable electronics. Industrial applications such as automotive systems benefit from more intelligent actuators, seals and valves that can withstand extreme conditions. A move into the body of a car made of form memory polymer composite materials can be obtained by using a hairdryer. By bridging innovative material science and practical product design, this research enables inexpensive, long -lasting solutions that meet the needs of state -of -the -art technologies. “
Li's book is made by Adithya Challapalli, a former Ph.D. Li student who graduated in 2023.
In the publication that soon published, Li and Challapalli explore innovative strategies for creating light architectures using cellular materials in load -bearing structures and potential to develop new cells with superior mechanical and architectural properties. By using machine learning and biomimicry – a method to emulate natural designs – the book contains new cell architectures for lattice substances with improved characteristics. The results indicate a significant improvement in the load transport capacities and the kink charging capacity, which contributes to the development of light architecture with better strength, stretching and tension restoration.
“This book is written with an audience from beginners to doctoral students in seniors, including practitioners,” said Li. “Gate structures, thin -walled structures, lattice structures and other porous structures are light technical structures that are widespread in aircraft, bridge decks, ship halls and others became. The design and optimization of technical structures are a routine task for mechanical engineers, civil engineers and chemical engineers. The modeling of finite elements and topological optimization are currently the main tools for the structural design. Due to the enormous construction space for technical structures, the traditional approaches are time -consuming and can most likely only find locally optimized structures.
“Machine learning is an emerging tool with which engineers can be supported in the design of technical structures with complex shapes, unique materials and tailor -made design requirements. This book is reference values for students, doctoral students and practitioners who are interested in using machine learning to design and optimize technical structures. The algorithms and methodology used in this book can also be used in other areas of studies, e.g. B. in machine learning to discover new materials. “
Since joining LSU in 1997, Li has written/co -authorized 251 referee journal papers. Overall, he wrote/co -authorized in his career 284. He also wrote/co-authorized three books, worked on three books and worked together and granted 20 US patents.