Data Science Usage in Mechanical Engineering

Mechanical Engineers today are excelling in every other domain. Analytics is not an exception. However let me tell you it’s a lot easier than mechanical engineering. If you can learn fluid mechanics, bending moment, law of gearing etc. then learning to play with data is child’s play. In fact here things are simpler, needs only two qualities, analytical intelligence and logics. If a device can collect lots of data, apply machine learning and can find patterns and address problems that might otherwise show up only after long phases of testing. Thermal, motion, positioning, pressure, optics, chemical and sound states are sensed and altered by sensors, transducers and actuators full of critical features requiring mechanical engineering to analyze, design, implement and test. Mechanical engineering is one of the most interesting discipline that makes interaction between AI algorithms and the real world possible. Henceforth it’s obvious there will be a huge demand for people who have both mechanical engineering and machine learning expertise. Once you find practical applications for the same, and you will never be out of a job for sure.

ML in mechanical engineering like Adaptive control involves reinforcement learning.
Separation between patterns equals clustering
Regression analysis plays an important role in mechanical engineering.
And when it comes to computational fluid dynamics, neural networks are the hottest new thing there.

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