Wonders Machine Learning can do for Industries

“Computers are able to see, hear and learn.  Welcome to the future.” ~Dave Waters

Machine Learning can be applied to just any control system which is smart enough to actually alter how it controls a machine in response to the changing conditions. Machine Learning can be leveraged to improve machine design, control systems, and production maintenance.
When considering machine learning for robotic and computer vision tasks, such as object recognition or pose estimation or involving the perception of complex motions, it can provide improved operation, and performance.

Implementation of Machine Learning:

  • Predictive Insights

The system process through a large amount of data to identify pattern and trends which are not visible using traditional tools. Besides providing insights, it aids in predictive monitoring – using algorithms one can forecast the equipment breakdown time and schedule a maintenance before that happens. With these predictions, the problem of unplanned downtime and machinery failure can be countered.
  • Performance Metrics

With the introduction of more compact and sophisticated sensors the system can be feed with unlimited data, using the analytics the performance can be measured and necessary action-plan can be devised.
  • Anomaly Detection

While working with large machines and complex computer systems it is possible to overlook things resulting in less accurate models and poor results, machine learning can be used to identify unusual patterns in the system and can be used to lay out an action plan to deal with the outliers.
  • Industrial Automation

Automation is actively becoming a part of our daily life. Machine learning can help in creating a smart machine. The best example of this is the Fanuc robot, the robot uses reinforcement learning to figure out how the given task has to be completed. The robot can be programmed and left overnight, it will figure out how to complete the task by morning.
  • Quality Control

Machine learning can be adopted for product inspection and quality control as well. By providing the system with the parameters regarding the quality of product and using a computer vision algorithm the good product can be distinguished from the flawed one.
  • Classification

The defects or abnormalities can be classified using machine learning. For example in a steel manufacturing plant, the system can use Artificial Neural Network (ANN)  to detect rust on steel bridge coatings. It can also be applied to detect internal defects in casting. Aside from that Convolutional Neural Network (CNN) can be used to detect and classify surface defects on the surface of steel strips.


Machine Learning is at the core of our journey towards Artificial Intelligence.
Automation, Robotic and complex analytics together are shaping the future of the industries, making them more sophisticated and smarter. The applications of machine learning are shining like a halo facilitating less equipment-failure, better on-time deliveries, advancement in quality, faster training cycles, advanced automation of design and production process.
All this adds up to form endless possibilities for the industries opening the gates to a brand new world of highly efficient, customer-driven factories that seamlessly connects to the surrounding smart ecosystem.

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