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Graph Learning for Cognitive Digital Twins in Manufacturing Systems

arXiv.org Artificial Intelligence

Future manufacturing requires complex systems that connect simulation platforms and virtualization with physical data from industrial processes. Digital twins incorporate a physical twin, a digital twin, and the connection between the two. Benefits of using digital twins, especially in manufacturing, are abundant as they can increase efficiency across an entire manufacturing life-cycle. The digital twin concept has become increasingly sophisticated and capable over time, enabled by rises in many technologies. In this paper, we detail the cognitive digital twin as the next stage of advancement of a digital twin that will help realize the vision of Industry 4.0. Cognitive digital twins will allow enterprises to creatively, effectively, and efficiently exploit implicit knowledge drawn from the experience of existing manufacturing systems. They also enable more autonomous decisions and control, while improving the performance across the enterprise (at scale). This paper presents graph learning as one potential pathway towards enabling cognitive functionalities in manufacturing digital twins. A novel approach to realize cognitive digital twins in the product design stage of manufacturing that utilizes graph learning is presented.


Digital Twins: Cross-Industry Applications - Intellias

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The concept of digital twinning has been around for quite some time, yet as its primary enablers, data analytics and IoT, are reaching maturity, digital twin applications are finally beginning to be used beyond manufacturing. As enterprises are increasingly harnessing the power of virtual prototyping, the trend is obvious. The only thing that remains unclear is how exactly digital twinning works in practice. Digital twinning is the use of technologies for mirroring physical systems through digital simulation. In essence, a digital twin is a piece of advanced software that operates on real-time data pulled from physical objects.


California utilities should have used digital twin technology instead of power shutoffs - POWERGrid International

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Northern California's proactive power outages were not necessary last fall. Digital Twin technology can predict utility line failures and turn off power in milliseconds to avoid the potential of sparks igniting the surrounding area. Digital twin technologies are gaining traction across industries and use cases. Initially devised as a means of monitoring assets and production settings in manufacturing, this technology has quietly seeped into other verticals like hospitality, construction, and building management and soon, electricity delivery. The premier problem digital twins will solve is predicting power grid failure, which would alleviate the social, economic, and political issues that resulted from efforts to reduce the incidence and degree of catastrophes, property loss, and deaths stemming from downstream effects of power grid failure--such as recurring wildfires.


Industry 4.0 and the digital twin

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Besides the areas of business values mentioned above, a digital twin may help address many other key performance and efficiency metrics for a manufacturing company. Overall, the digital twin may offer many applications to drive value and start to fundamentally change how a company does business. Such value may be measured in tangible results that may be tracked back to key metrics for a business. Much of the discussion thus far has focused on a digital twin model of the manufacturing process portion of the product life cycle. The manufacturing process represents but one digital twin configuration.


GE Expands Predix Platform to Advance Industrial Internet Opportunities for Customers

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SAN FRANCISCO – November 15, 2016 – Today at Minds Machines, GE (NYSE: GE) announced new products, acquisitions and partner programs to enable further adoption of Predix, the operating system for the Industrial Internet. The platform enhancements, acquisitions and new ISV partner program further complement the Predix technology stack and make it easier for industrial companies to execute a strategic digital transformation to drive internal productivity. In 2016, orders from GE's portfolio of software solutions are on track to climb 25% to more than $7 billion – making GE the fastest growing digital industrial company in the world. Demonstrating the strength of Predix within GE, digital thread productivity will exceed $600 million, accelerating into 2017. "The opportunity for industry is now," said Bill Ruh, Chief Digital Officer of GE and CEO, GE Digital.