Manufacturers Should Know These AI Trends

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Artificial Intelligence (AI) has been around as an idea for several decades, but only recently has it begun to transform a number of industries, including manufacturing. The introduction of smart robots on the assembly line, intelligent machine diagnostics, automated warehousing and AI-assisted inventory and supply chain management are just the beginning.

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AI is leading to new business models, better products and improved services and experiences. Research by Microsoft shows that organisations already embracing AI are delivering better productivity and performances than those that are not.

Some key AI trends are outlined below.

Maintenance

Research has shown that unscheduled downtime costs manufacturers a staggering $50 billion per year and that asset failure is responsible for 42 percent of this.

As a result, smart/predictive maintenance has become a key solution for manufacturers. Using AI algorithms, predictive maintenance is able to formulate predictions about asset malfunction. This can realise huge savings by drastically reducing unplanned downtime.

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Quality 4.0

By using AI algorithms, Quality 4.0 is able to identify potential production faults that could cause quality issues. These include abnormalities in machine behaviour, deviations from recipes, changes in raw materials and many others. By being able to predict/identify these issues at early stages, higher quality can be maintained.

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Human-Robot Collaboration

Advances in AI will mean that robots can take on many of the more basic and repetitive tasks, freeing up humans for more complex roles involving design, maintenance and programming. As well as this, robots will be able to work alongside humans on the production floor, helping with things like quality control and more efficient packing. As robots become more intelligent, they will also be able to take on more complex tasks and make decisions based on real-time data.

Improved Products

AI is changing the way products are designed. One way is through inputting a detailed brief into an AI algorithm that then explores all possible configurations before identifying the best solutions. These can then be tested virtually, offering extra insight as to which should work best. This approach also removes any subjectivity associated with human designers.