Whether you like it or not, the era of robots and intelligent machines is here.
What machine knowledge will generate in the future is beyond our scope of imagination.
As Joe Hellerstein, Co-Founder, and CSO of Trifacta said for Forbes business magazine: in 2020, the focus of machine learning will shift from research to engineering.
The impact of these technologies is changing SaaS, manufacturing, business, and other industries.
In the first place, this scientific study produced new engineering roles.
ML engineers develop systems that apply learned data in contrast to general intelligence.
Artificial Intelligence (AI) engineers work with neural networks, tools, and algorithms to advance ML and AI technologies.
ML Technologies are expanding to all engineering professions.
The expansion requires engineers to adapt to the requirements it's bringing.
Therefore, answer to the main question is - yes – mechanical engineers can do machine learning in many fields of projecting, designing and construction.
If you're considering ML as a strategy for your products and services, you must improve your programming knowledge.
The most important step you must tackle is to get knowledge in appropriate programming languages.
You can find an analysis of the best solutions for ML engineering on relevant forums.
Your base should be to get a good grip on Python, R, C++, and Java.
Many engineers struggle to find a connection between ML and engineering to boost their designs. This is a common scenario, especially when it comes to mechanical engineering.
Yet, there are many associations for you to link it with mechanical engineering. For instance, reinforcement learning connects with adaptive control.
System capabilities for achieving high performing modes in adaptive controlling. Reinforcement learning trains algorithms for the same aim.
There are many other examples of ML integration in mechanical engineering. Separation between patterns is resembling clustering.
ML tools improve many fields of mechanical engineering. Regression analysis, algorithms for classification methods, etc. are one of them.
Fields most promising for improvement with ML technologies are reverse engineering and designing.
If you design mechanical parts, you know that computers understand complex designs better. Skilled engineers use optimization tools and methods for new effective outputs.
ML brings new methods for design by combining system analyses of mechanics and physics.
If reverse engineering is your field of interest, machine learning might be one of your high priority considerations.
ML improves your ways of determining procedures and analyze manufacturing objects.
This could shorten your time between remodeling and designing, and you could reach your goals, and sell your products faster.
Finally, it can help you state mechanical system behavior. This feature can help you to predict the performance of components complicated to model.
By having this information, you can maximize the durability of your components and predict the future of your systems.
AI and ML community continues to expand, and with that, you will have more opportunity to manipulate data and build proficient mechanical parts and models.
There is a popular opinion that AI is a golden ticket for optimizing your designs and end products.
Yet, you shouldn't accommodate with once learned information, because AI is set to evolve.
Your ability to adapt and learn about ML will boost your design building and improve your system performance.