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Pictures of the Future


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Pictures of the Future
The Magazine for Research and Innovation

The Future of Manufacturing

A Simulation Toolbox that’s Transforming Manufacturing

Experts predict that in the future PLM software from Siemens will enable entire production lines to reconfigure themselves in response to changing demands.

Product Lifecycle Management software from Siemens is transforming the way products and production lines are created. The key to this is the concept of the “digital twin” – a replica in the virtual world of a future or existing object or process. Because of the opportunities they provide for visualization and optimization, such twins, when combined with artificial intelligence, could open the door to the possibility of autonomous reconfiguration of production processes in response to rapidly changing customer demands.

Bottlenecks are bad for business – and are all too common. Suppose, for instance, that a delivery of seats at an automobile production line is delayed by several hours. If sufficient reserve seats are not on hand, the entire line may grind to a halt.  

Although such problems are an age-old scourge, they are set to become far less likely thanks to the advent of production environments that rely on digital information. “Tomorrow’s factories will be characterized by three elements that will make their information flow flexibly, yet seamlessly,” says Zvi Feuer, Senior Vice President for Product Lifecycle Management (PLM) software at Siemens. “The systems will be extensively networked, individual elements — meaning machines and robots — will be mobile, and they will be able to reconfigure themselves.”

In the future, production lines will be able to independently react to changing demands, such as a request for a different vehicle for which all parts are in stock.

Something Fantastic

Although this vision of the future of manufacturing may – with the exception of few showcase factories like Siemens’ digital factory in Amberg – still be years away, things are clearly headed in the right direction. For instance, before Feuer’s customers began using PLM software, they often lost considerable time and money as a result of late deliveries. But as they have moved toward fully digital environments based on PLM software they have increasingly created “digital twins” of all of their products’ manufacturing steps: “The digital twin is the key to almost all of the things we mean when we talk about digital factories,” says Feuer. Such twins can be created not only for products, but for entire production systems – for instance to quickly determine how many car seats are still in the warehouse, and thus how long production can continue without fresh deliveries. Instead of simply stopping production, PLM forecasts how long it makes sense to continue manufacturing a given item.

Thanks to PLM software, users can perform virtual tests by creating digital twins of all the steps in a product's manufacturing process.

“But eventually, as standards for machine-to-machine communication are implemented, something really fantastic will happen,” says Feuer: “The entire assembly line will reconfigure itself so that it can temporarily produce a different vehicle for which parts are in stock.” Siemens PLM has already undertaken important steps for establishing those standards. For example, for years it was almost impossible to transfer CAD files between companies due to the fact that there was no standard for CAD file transfer. “Therefore, we developed a natural 3D geometry standard named JT that is now an ISO standard and that supports a natural transfer for CAD files”, Zvi explains.

Coding for Reconfiguration

Manufacturers are increasingly under pressure to make their assembly lines and associated logistics easily reconfigurable from one day to the next. Ideally, to accomplish this, systems should be able to reorganize themselves. This would not only help to obviate delivery bottlenecks, but, more fundamentally, would help to ensure survival in an increasingly competitive market.  

To set the stage for this new world of flexible manufacturing, production facilities will need loads of data – real-time data regarding what parts are needed where and when, how to process each part, , and how different configurations of machine tools and robots would affect a facility’s power demand and the positioning of associated cables.

Today, as ever more production machines and processes are networked and deliver streams of information, the dream of self-configuration is approaching. But for the time being, machines still have to be told what to do. This process takes place via control codes. PLM already provides simulations that help programmers create such codes. However, the codes still need to be written by human beings due to their complexity. To create such codes, programmers need to know where each machine is located, how machines are grouped, and each machine’s service history. This information is needed before machines can be reprogrammed and reorganized into new configurations. “We call this a feedback loop,” explains Feuer. “We need it in order to link current processes in a factory with those that will be performed in the future.”

OptoTech used Siemens PLM Software to design, develop and optimize the world’s biggest and most advanced precision optics machine. The machine is used in the production of high-precision telescope mirrors.

Copy-and-Paste Production Systems

Simulation of feedback loops begins with data from sensors in production and logistics environments and takes place in the cloud. Users can solve virtually any scenario with the help of PLM and the associated digital twins. For example, when a manufacturer is expanding its production operations and wants to build a second production hall in order to double its capacity, PLM enables the user to, in a sense, copy and paste production systems. “We can set up an identical facility anywhere in the world and do so with the same precision,” says Feuer. As Feuer’s customers have repeatedly confirmed, the resulting level of precision is is impressive.  

In recent years, thanks to acquisitions and its own research and development activities, Siemens PLM has substantially increased the capabilities of digital twins. “These simulations have become increasingly comprehensive,” says Feuer. “They now include detailed information not only on a product’s geometry, but also on its mechanical and electronic operations. The result is that we are getting closer and closer to the literal meaning of the word twin.” To this end, Siemens PLM is also working closely with a number of partners (see box).

According to Feuer, artificial intelligence will play an ever greater role in PLM in the future. “We are currently in the midst of an exciting development in which machines’ ability to learn is steadily increasing,” he says. As a result, the biggest challenge that automation currently faces is to enable machines to write new control codes themselves. “Although we aren’t quite there yet, we already know how it will be done,” says Feuer.

Sandra Zistl
Picture credits: from top: 1 and 2. picture: shutterstock, 4. NASA