Artificial Intelligence in Manufacturing

Artificial Intelligence in the Modern OEM

October 7, 2019

Artificial Intelligence (AI) is upon us. Today we encounter AI in our daily lives through virtual assistants Siri or Alexa, writes Forbes magazine, but it’s making its way into manufacturing faster and easier than may have been originally expected.

Nordcloud, a provider of public cloud infrastructure and managed services, has tallied 10 use cases of AI at work in manufacturing, to help your OEM business find inspiration.

1. Quality checks

Factories creating intricate products like microchips and circuit boards are making use of ‘machine vision’, which equips AI with incredibly high-resolution cameras. The technology is able to pick out minute details and defects far more reliably than the human eye. When integrated with a cloud-based data processing framework, defects are instantly flagged and a response is automatically coordinated.

2. Maintenance

Smart factories like those operated by LG are making use of Azure Machine Learning to detect and predict defects in their machinery before issues arise. This allows for predictive maintenance that can cut down on unexpected delays, which can cost tens of thousands of pounds.

3. Faster, more reliable design

AI is being used by companies like Airbus to create thousands of component designs in the time it takes to enter a few numbers into a computer. Using what’s called ‘generative design’, AI giant Autodesk is able to massively reduce the time it takes for manufacturers to test new ideas.

4. Reduced environmental impact

Siemens outfits its gas turbines with hundreds of sensors that feed into an AI-operated data processing system, which adjusts fuel valves in order to keep emissions as low as possible.

5. Harnessing useful data

Hitachi has been paying close attention to the productivity and output of its factories using AI. Previously unused data is continuously gathered and processed by their AI, unlocking insights that were too time-consuming to analyse in the past.

6. Supply chain communication

The aforementioned data can also be used to communicate with the links in the supply chain, keeping delays to a minimum as real-time updates and requests are instantly available. Fero Labs is a frontrunner in predictive communication using machine learning.

7. Cutting waste

Steel industry uses Fero Labs’ technology to cut down on ‘mill scaling’, which results in 3 percent of steel being lost. The AI was able to reduce this by 15 percent, saving millions of dollars in the process.

8. Integration

Cloud-based machine learning – like Azure’s Cognitive Services – is allowing manufacturers to streamline communication between their many branches. Data collected on one production line can be interpreted and shared with other branches to automate material provision, maintenance and other previously manual undertakings.

9. Improved customer service

Nokia is leading the charge in implementing AI in customer service, creating what it calls a ‘holistic, real-time view of the customer experience’. This allows them to prioritise issues and identify key customers and pain points.

10. Post-production support

Finnish elevator and escalator manufacturer KONE is using its ‘24/7 Connected Services’ to monitor how its products are used and to provide this information to its clients. This allows them not only to predict defects, but to show clients how their products are being used in practice.

AI in manufacturing is reaching a wider and wider level of adoption, and for good reason. The technology is here and the research is ready – how will AI revolutionise your industry?

post excerpted from Nordcloud Tech Blog, “10 Examples of AI in Manufacturing to Inspire Your Smart Factory.”