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Machine learning in manufacturing

Artificial intelligence has been advancing by leaps and bounds. Computers can now process and translate languages, detect objects, identify faces and so much more. AI and machine learning are going to revolutionize manufacturing, and companies could be spending as much as $47 billion on these technologies by 2020. Read on for more information on machine learning in manufacturing.Blog Img_Machine learning in manufacturing_IQS

What is machine learning?

Machine learning is surprisingly similar to the way humans learn. When we face something that is new and unknown, we use something learned from a previous experience to come to a conclusion about how to treat this new an unknown experience.

In much the same way, machine learning in manufacturing allows machines to behave in a similar manner. Machine learning takes place in a controlled environment where humans carefully select the data available to the AI and set a desired outcome. Data is fed into software that develops an algorithm. This algorithm can then be used to make predictions of future outcomes.

Another form of machine learning requires very little human input. Instead, the program is provided with large datasets without a desired outcome. This process is known as deep learning, and is more suited for complex tasks.

How is machine learning used?

The neural network developed by Google received a lot of attention a couple of years ago because engineers built an AI that could create psychedelic art. While this wild experiment demonstrated the endless possibilities of machine learning, you should know that there are also many practical applications, some of which have been around for a while:

  • Search engines use machine learning to consistently improve search results and to show relevant ads.
  • Most social media platforms now use machine learning to filter posts and show you the content that is most popular rather than showing a chronological timeline.
  • Websites like Amazon or Netflix using machine learning to show personalized recommendations based on your activity.
  • Being able to recognize anomalies makes machine learning a powerful tool for credit card fraud detection, airport security and network security.
  • Machine learning allows driverless cars to adapt to their environment.
  • IBM is currently changing the healthcare field with its Watson AI. The AI uses data to predict the best cancer treatment plan for each patient and to match patients with clinical trials.
  • Automation and machine learning are making it possible for pharmaceutical companies to implement advanced quality control systems and to develop personalized treatments for each patient.

The manufacturing field is a prime candidate for machine learning because most manufacturing companies already have at least limited experience adopting automation and using the Internet of Things for their production line. The pre-existence of large datasets gathered for analytic purposes also lends itself to using machine learning to improve efficiency.

Machine learning in manufacturing and quality control

Machine learning in manufacturing will revolutionize quality control practices. AIs can detect an anomaly in real-time, accurately classify it and take action by shutting down the production line, for instance. Machines can even replace visual inspection by analyzing the appearance of an object, identifying flaws and categorizing the detected flaws.

Besides being able to use machine learning on the floor, AI findings could also be used for predictive maintenance, to predict product quality and to improve product design.

Digital integration is still an obstacle for a lot of companies that have not yet fully automated their production line. Manufacturers may need to invest in the Internet of Things and rethink their production process before they can benefit from machine learning.

A final roadblock to machine learning is the personnel required: Having access to the right talent in sufficient numbers can be difficult, since developing software that can learn and produce a reliable outcome requires advanced programming skills.

Machines can use algorithms to learn an existing dataset and predict outcomes or identify anomalies. This technology has already transformed fields such as marketing, healthcare, manufacturing and packaging. In the near future, machines could be in charge of the quality control process that happens on the production floor.

Manufacturing Metrics in an IoT World 2016_IQS


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