How AI is revolutionizing manufacturing

From predictive maintenance to digital twins, artificial intelligence is ushering in the next manufacturing revolution — if not for shortages of skills, data, and standards.

How AI is revolutionizing manufacturing

Artificial intelligence and machine learning are transforming the manufacturing industry. According to a report released late last year by the World Economic Forum, the combination of artificial intelligence, advanced robots, additive manufacturing and the internet of things (IoT) will combine to usher in the fourth industrial revolution.

The majority of manufacturing companies, 80 percent, expect to see positive effects of AI initiatives, with a predicted increase in revenue of 22.6 percent, and a 17.6 percent reduction in costs.

In fact, manufacturers are already using artificial intelligence and machine learning technologies to reduce equipment downtime, spot production defects, improve the supply chain, and shorten design times. However, the lack of skilled personnel, data, and standards is keeping many companies from forging ahead.

General Electric leads the charge

One of the companies at the forefront of this new wave of industrial transformation is General Electric, which has been motivated to explore the use of AI due to declining productivity in its sector.

"Up until 2010, productivity growth was in the 4 to 5 percent range," says Colin Parris, the company's vice president of software research. Then the industry changed. Experienced engineers were retiring, while the new geographies GE was in, including India and China, had primarily a young workforce.

Meanwhile, Parris says, customer requirements were rapidly becoming more complex. There were new routes to destinations with extreme weather conditions and air pollution that affected the jet engines that GE produced. And social media magnifies the impact of any outage, forcing customers to demand better reliability and less downtime.

At the same time, customers expected prices to continue to fall.

"People say you can't predict the future," Parris says. "You definitely can. People want things cheaper."

To address this issue. General Electric turned to artificial intelligence and machine learning, starting with services it provided to its customers, such as jet engine and turbine maintenance. Then GE applied AI to internal manufacturing, followed by design, and then internal processes, such as data center operations and human resources.

"We've been using models and forms of analytics in services for last 10 to 15 years at least," says Parris. Five years ago, GE began using machine learning and digital twins, which provide a virtual representation of a piece of machinery, such as a wind turbine, or a grouping, such as a wind farm. Digital twins can also be used to represent an assembly line, an entire factory, or a procurement process.

At GE, digital twins are used to model performance, predict failures, and allow for rapid testing of potential improvements.

"We can predict which things are going to fail, so we have the right engineers, and we have the right parts in inventory," Parris says. "We can get [better] fuel efficiency and fly planes longer without bringing in parts for service unnecessarily. We've been saving millions of dollars for customers."

Another side benefit of having a digital twin of every piece of equipment, system or process was that GE could take advantage of additive manufacturing — 3D printing — to create custom parts, instead of having to rely on replacement parts that had to be manufactured in bulk, on traditional assembly lines.

"As the years go on, the machines degrade differently," he says. "Now I can say, 'Can I design parts specifically because in this machine used in this way we're seeing damage in the training edge, or more cracking in this blade?' Additive manufacturing allows me to build one part at a time, to solve the unique problems that this machine is having in this environment, rather than having to build these huge factories and turn out hundreds of these parts that will work on average. Before, I had to spend hundreds of millions of dollars to build the factories. Now I can print one part at a time, and can constantly adapt the body of the machine, and the mind of the machine. Now I have a machine that can continuously adapt itself to be more and more productive, what we call an immortal machine."

"This is where I think the future gets very interesting for GE," he adds.

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