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Machine Learning, Artificial Intelligence, and Computation in Mechanical Engineering
Machine Learning, Artificial Intelligence, and Computation in Mechanical Engineering
“Big breakthroughs happen when what is suddenly possible meets what is desperately necessary.”
Thomas Friedman
New York Times, 2012
Mechanical and Artificial Intelligence laboratory (MAIL) at CMU is inherently a multidisciplinary group bringing together researchers with different backgrounds and interests, including Mechanical, Computer Science, Bio-engineering, Physics, Material and Chemical Engineering.
Our mission is to bring the state of the art machine learning algorithm to mechanical engineering. Traditional mechanical engineering paradigms use only physics based rules and principles to model the world which does not include the intrinsic noise/stochastic nature of the system. To this end, our lab is developing the algorithms that can infer, learn and predict the mechanical systems based on data. These data-driven models incorporate the physics into learning algorithm to build more accurate predictive models. We use multi-scale simulation (CFD, MD, DFT) to generate the data.
Mechanical engineers can use artificial intelligence (AI) in a number of ways to improve the design, analysis, and control of mechanical systems. Some examples of the applications of AI in mechanical engineering include:
Design Optimization: AI algorithms can be used to optimize the design of mechanical components and systems by considering a large number of design variables and constraints. For example, genetic algorithms and particle swarm optimization can be used to optimize the shape and size of mechanical components to meet specific performance criteria.
Predictive Maintenance: AI algorithms can be used to predict the failure of mechanical components and systems based on data from sensors and historical data. This can help prevent unplanned downtime and reduce maintenance costs.
Quality Control: AI algorithms can be used to automatically inspect and classify mechanical components for quality control. For example, computer vision algorithms can be used to detect defects in castings and other manufactured components.
Autonomous Systems: AI algorithms can be used to control autonomous systems such as drones and robots. For example, machine learning algorithms can be used to train robots to perform specific tasks, such as welding or painting.
Energy Management: AI algorithms can be used to optimize energy consumption in mechanical systems, such as HVAC systems and refrigeration systems. For example, neural networks can be used to predict energy demand and control energy consumption based on the predictions.
Overall, AI has the potential to greatly improve the efficiency, reliability, and performance of mechanical systems, and many mechanical engineers are exploring its potential in their work.
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