How Big Data Makes Us Rethink The Design Of Magnetic Devices

One of the many things big data has transformed over the years is the design of magnetic devices. Here's how it's made an impact, and why.

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June 7, 2019
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One of the most fascinating things about big data is its ability to optimize the design of products that have pre-dated digital technology by centuries. One of the most interesting examples is with magnets.

Magnets are ancient devices. They are so old, that the history of their discovery has been lost in legend. It is rumored that the first magnet was discovered by a Cretan man named Magnes over 4000 years ago.

While magnets have been part of modern civilization for millennia, they are constantly being perfected and re-designed. Improvements were needed for imaging and data storage. Further improvements are needed for other applications. New advances in big data and artificial intelligence are making magnets more versatile and cost-effective to consumers and industrial leaders on either side of the globe. They could even impact the designs of custom printed magnets in the future.

Fujitsu leads the way with AI solution to better magnetic material geometries

Fujitsu is currently the fourth largest IT service provider in the world. Located in Japan, the company has an unprecedented track record for innovation. The company was founded in 1935, but it has maintained a strong competitive edge by staying up-to-date with major advances in information technology.

Fujitsu has recently started embracing the benefits of big data. In May 2018, Fujitsu engineers published a paper on their utilization of artificial intelligence in magnetic material design. They said it would be particularly important in finding the ideal size constraints and shapes of magnets.

Artificial intelligence is going to be paramount to the research and development process for Fujitsu. They have discovered that it can play an important role in reducing energy loss. It is also helping them improve geometric magnet design. Prior to their new design specifications, these types of designs required extensive expertise. This is changing as artificial intelligence is being used to improve design outcomes. With the new AI models in place, less proficient design experts can create magnetic designs with optimal efficiency.

What are the benefits of big data in the design of magnetic devices?

Fujitsu is not the only company that is interested in exploring the benefits of big data in the design of magnetic products. Other companies are exploring the benefits as well.

Here are some of the reasons that artificial intelligence is leading to higher quality magnetic designs.

Predictive analytics helps engineers anticipate future applications and the necessary design parameters

For most of the 20th and 21st centuries, companies designing new products had to take a backwards-looking process. They needed to use empirical data from previous projects and tweak design elements to the best of their ability to meet new standards.

This was an imperfect approach because technology has never been static and customer needs are constantly evolving. Industrial partners need to utilize more sophisticated technology, which depends on more scalable design constraints.

As a result, magnets that had sufficient power to handle applications one year might be impractical a few years later. Predictive analytics is helping designers tackle this challenge.

New predictive analytics models are able to forecast the load bearing, energy storage and other design requirements for future applications. This will help engineers understand all of the material requirements that need to be satisfied, including magnetic geometries and sizes.

Improve cost-effectiveness

Cost-effectiveness is another factor that cannot be overlooked in any design project. Projects requiring magnetic materials are no exception.

This is one of the factors that engineers do you have to always take into consideration. Design engineers and manufacturing engineers sometimes find themselves at odds. Design engineers might be tempted to design products with higher than necessary factors of safety and performance KPIs because they are expected to prioritize functionality and minimize failure rates.

The problem is that they might use more material than necessary, which can make the final design much more expensive. They need to carefully assess their design to make sure it meets functionality requirements, without costing a fortune.

This is another area where big data can be especially invaluable in the design of magnetic materials. New data-driven design processes can help make sure that they don’t use more material than necessary, while still meeting the design requirements.

AI is Changing the Future of Magnetic Designs

Magnets have been central to the design of many products of hundreds of years. They are evolving considerably in 2019. Big data is playing a very important role in the way engineers use them.