Artificial Intelligence Optimization offers a host of benefits for mechanical engineers, including sharp and more accurate design and simulation, better efficiency, procedure automation through automation, reduction in growth costs, and future stating maintenance and increase in quality control.
“When people think about mechanical engineering, they are thinking about basic mechanical equipment such as Hammers and … Cars, Robots, Cranes, but mechanical engineering is very widespread,” Fesne Ahmed says, DOHRI CHARCER and Mechanical Engineering Associate Professor in Ocean’s use. “Mechanical engineering, machine learning, AI and optimization are playing a big role.”
In Ahmed’s course, 2.155/156 (AI and machine learning for engineering design), students use equipment and techniques from artificial intelligence and machine learning for mechanical engineering design, focus on manufacturing new products and face engineering design challenges.
play video
Cat Trees to Motion Capture: For AI and ML Engineering Design
Video: MIT Department Mechanical Engineering
“There are a lot of reasons for mechanical engineers to think about machine learning and AI, which are essentially accelerating the design process,” a teaching assistant for the course and Ahmed’s design compute and Digital Engineering Lab (Decode) says PhD candidate, where research focuses on developing new machine learning and adaptation methods to study complex engineering design problems.
The first was introduced in 2021, the class has quickly become one of the most popular non-core offerings of the Department of Mechanical Engineering (MECHE), attracting students from the departments of the institute, including mechanical and civil and environmental engineering, aeronautics and astronautics, MIT Slon School of Management and MIT Slon School of Management and Nuclear and Computer Sciences, including Harvard University and other schools.
The curriculum, which is open to both graduates and graduate students, focuses on the implementation of advanced machine learning and adaptation strategies in terms of real -world mechanical design problems. From designing bike frames to city grids, students participate in AI -related competitions for physical systems and deal with optimization challenges in a class environment fuel by friendly competition.
Students are given a challenge problems and starter code which “a solution, but [not] The best solution… ”Ilaan Moyer, a graduate student in Make, tells.” Our job was [determine]How can we do better? “Live leaderboard encourages students to continuously refine their ways.
Bachelor of System Design and Management Students, M. Lubber, say that the process was learning what students were learning and literally placed the practice skills of “how to code it”.
The curriculum involves discussion on research papers, and students also practice hand learning in machine learning to suit specific engineering issues including robotics, aircraft, structures and metamatorials. For their final project, students work together on a team project that appoints AI techniques for design on a complex problem of their choice.
“It is amazing to see various widths and high quality of class projects,” says Ahmed. “Student projects often lead to research publications from this course, and even awarded prizes.” He recently cites an example of a paper, titled “Gankad-Self-Repairing”, who went to win the American Society of Mechanical Engineers Systems Systems Engineering, Information and Knowledge Management 2025 to win the best paper award.
“The best thing about the final project was that it gave every student an opportunity to implement what he has learned in an area in class, which is very interested in him,” says Malia Smith, a graduate student in Mache. ” His project chose the “marker motion captured data” and saw the ground force predicting for the runners, an attempt that he called “really satisfying” because it worked much better than expected.
Lubber took a “cat tree” design structure with various modules of poles, platforms and ramps to create adapted solutions for individual cat homes, while Moyer created software that is designing a new type of 3D printer architecture.
“When you see machine learning in popular culture, it is very abstract, and you have an understanding that something very complex is going on,” Moyer says. “This class has opened curtains.”
