Digital transformation: Leading by example
With the Digital Enterprise, Siemens is consistently pursuing the digital transformation of industry – both at the customer’s site and in-house. Take, for example, the Siemens Electronics Works Amberg (EWA). Whether it’s optimized throughput, ambitious cycle times, or reliable security measures, the future of manufacturing is already a reality in many areas of the EWA, thanks to numerous solutions from the Digital Enterprise portfolio.
Given the 350 production changeovers per day, a portfolio containing roughly 1,200 different products, and 17 million Simatic components produced per year, about 50 million items of process and product data need to be evaluated and used for optimization in order for production at the Siemens Electronics Works Amberg (EWA) to run smoothly. In addition, groundbreaking technologies like artificial intelligence (AI), Industrial Edge computing, and a cloud solution are already enabling highly flexible and extremely efficient and reliable production sequences.
Industrial Edge computing and AI for increased throughput
“With Edge Computing, data can be immediately processed where it’s generated, right at the plant or machine,” says Dr. Jochen Bönig, Head of Strategic Digitalization at Siemens Amberg. This is what EWA is doing, for example, on the production line where PCBs are manufactured for components of the distributed I/O.
But even here, production isn’t sufficiently optimized, and it’s neither the fault of plant availability nor process quality. The bottleneck is at the end of PCB production, at the automatic x-ray inspection section.
Circuit boards of the size of a fingernail accommodate function-related BUS connectors with various connecting pins. In a non-integrated test, the soldered joints of these connecting pins are x-rayed and checked for correct functioning. Should another x-ray machine be purchased for about €500,000?
The alternative is artificial intelligence. The data from the sensors is transferred to a cloud via the TIA (Totally Integrated Automation) environment, which consists of a controller and an Edge device. Experts train an algorithm that’s based on AI and the process parameters. The algorithm learns how process data reflecting the quality of the soldered joints behaves and controls a model that runs on an Edge application at the plant.
“The model predicts whether or not the soldered joints on the PCB are free of faults: in other words, whether or not an end-of-line test is necessary. Thanks to closed-loop analytics, this data can immediately be factored into production,” explains Bönig.
Early warning system prevents unpleasant surprises
Closed-loop analytics and Industrial Edge technology are also used in milling. The milling spindle for depaneling PCBs for Simatic products wasn’t always functioning correctly due to milling dust, but initially the cause was unclear. As with the automatic x-ray inspection, the Siemens experts relied on a combination of Edge computing and AI for predictive maintenance.
The team isolated two parameters that were clearly related to the unscheduled downtime: the rotational speed of the milling spindle and the electric current required by the drive. This data was fed into an Edge device where a pretrained algorithm identified interrelationships between anomalies in the process data and downtime and fed them back into production.
The Performance Insight app makes the results available to users in MindSphere, the open, cloud-based IoT operating system. Plant operators are now informed of the situation 12 to 36 hours before a potential system failure and can respond accordingly.
But the data and anomalies aren’t simply stored in MindSphere. The algorithm has to be better trained so that it can deliver results that are increasingly precise. “That’s exactly what happens in MindSphere. The consistent, end-to-end digitalization environment at EWA ensures the necessary seamless interaction between automation, Industrial Edge, and cloud computing,” explains Florian Meierhofer, an IoT Expert at EWA.
Digital twins and proof of concept (PoC)
The digital twin is responsible for the fact that Simatic controller components are now produced within a target cycle time of eight seconds. The initial simulation promised a cycle time of eleven seconds. It also revealed that the eleven-second cycle time was mainly attributable to machine modules that were planned for the line but didn’t work optimally in the actual production.
Siemens experts replaced these modules with more appropriate components in the digital twin of production. In the next the simulation, the target cycle time was seamlessly achieved, thereby confirming the proof of concept.
Prime example of the Digital Enterprise
Summary: At the Siemens Electronics Works Amberg, hardware and software solutions, industrial communication, cybersecurity, and services are optimally coordinated. Production sequences are seamless, thanks to consistent, end-to-end horizontal and vertical integration. This makes EWA a prime example of a Siemens Digital Enterprise that will continue to consistently lead the way into the future digital transformation.
The Siemens Electronics Works Amberg (EWA) was founded in 1989 and manufactures products that include Simatic programmable logic controllers (PLCs). Every year, the factory manufactures about 17 million Simatic products, meaning that one product is dispatched every second. Over 1,000 product variants are manufactured there. They’re used to control plants and machinery and automate production facilities, saving time and costs and improving product quality.
Production processes at Amberg itself are also controlled by about 2,800 Simatic components. Production functions on a largely automated basis, with 75 percent of the value chain handled independently by machines and robots. In any 24-hour period, products are prepared for dispatch to approximately 60,000 customers worldwide. EWA manufactures to a quality standard of 99.9990 percent. As the result of sustainable work quality and comprehensive data integration, this achievement sets a benchmark for industrial production.