Siemens

Software : Operational Technology : General

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Munich, Bavaria, Germany

ETR: SIE

As a focused technology company, we combine the real and the digital worlds and help customers to meet the great challenges of our time. Our businesses and local organizations enjoy the entrepreneurial freedom to serve their customers and markets in the best way possible, the structure is geared toward creating value for customers, creating technology with purpose and thus changing the lives for billions of people for the better. We create technology to transform the everyday.

Assembly Line

The Metaverse Goes Industrial: Siemens, NVIDIA Extend Partnership to Bring Digital Twins Within Easy Reach

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Topics: Metaverse, Digital Twin

Organizations: Siemens, NVIDIA

Silicon Valley magic met Wednesday with 175 years of industrial technology leadership as Siemens CEO Roland Busch and NVIDIA Founder and CEO Jensen Huang shared their vision for an “industrial metaverse” at the launch of the Siemens Xcelerator business platform in Munich. Pairing physics-based digital models from Siemens with real-time AI from NVIDIA, the companies announced they will connect the Siemens Xcelerator and NVIDIA Omniverse platforms.

The partnership also promises to make factories more efficient and sustainable. Users will more easily be able to turn data streaming from the factory floor PLCs and sensors into AI models. These models can be used to continuously optimize performance, predict problems, reduce energy consumption, and streamline the flow of parts and materials across the factory floor.

Read more at NVIDIA Blog

Siemens acquires Brightly Software to accelerate growth in digital building operations

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Organizations: Siemens, Brightly Software

Siemens Smart Infrastructure (SI), the frontrunner in digital buildings, has signed an agreement to acquire Brightly Software, a leading U.S.-based software-as-a-service (SaaS) provider of asset and maintenance management solutions. The acquisition elevates SI to a leading position in the software market for buildings and built infrastructure. The purchase price is USD 1.575 billion, plus an earn-out. The acquisition will add Brightly’s well-established cloud-based capabilities across key sectors – education, public infrastructure, healthcare, and manufacturing – to Siemens’ digital and software know-how in buildings.

Read more at Siemens Press Release

Siemens buys UK industrial IoT firm Senseye for global smart factory push

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Author: James Blackman

Organizations: Siemens, Senseye

Siemens has acquired UK-based industrial IoT firm Senseye for an undisclosed fee. Senseye, founded in 2014, provides analytics-based (“AI-powered”) predictive maintenance solutions for industrial machines, offering ways to manage and reduce unplanned downtime and to boost productivity and sustainability. The firm, headquartered in Southampton, was picked up by Zurich-based venture firm Momenta Partners as an early portfolio company; it claims its IoT sensing and analytics product, available on subscription (as-a-service), reduces unplanned machine downtime by up to 50 percent and increases maintenance staff productivity by up to 30 percent.

Read more at Enterprise IoT Insights

Robots Become More Useful In Factories

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Author: John Koon

Topics: Industrial Robot

Organizations: Siemens

“The main focus of manufacturing is to increase productivity measured in throughput over a time period, with minimum downtime,” said Sathishkumar Balasubramanian, head of product management and marketing for IC verification at Siemens EDA. “But assembly line manufacturing line is a dynamic environment, and automation is only part of the solution. On the outside, it seems to be important to have constant flow. However, variability in manufacturing flow is inevitable, and how the manufacturing process adapts to variation is highly critical to keep the downtime to a minimum. For example, in bottling manufacturing, how the work moves from station 1 to station 4, and a change in bottle orientation, can be addressed by an adaptive production line to meet peak demand with minimum disruption. That is very important. The ability to sense the status of manufacturing line at the edge is key to robotic manufacturing process.”

Read more at Semiconductor Engineering

Digital twins improve real-life manufacturing

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Author: James Vincent

Organizations: Siemens, Tesla, Boeing

Real-world data paired with digital simulations of products—digital twins—are providing valuable insights that are helping companies identify and resolve problems before prototypes go into production and manage products in the field, says Alberto Ferrari, senior director of the Model-Based Digital Thread Process Capability Center at Raytheon.

The concept has started to take off, with the market for digital-twin technology and tools growing by 58% annually to reach $48 billion by 2026, up from $3.1 billion in 2020. Using the technology to create digital prototypes saves resources, money, and time. Yet the technology is also being used to simulate far more, from urban populations to energy systems to the deployment of new services.

Read more at MIT Technology Review Insights

Industrial Organizations Targeted in Log4Shell Attacks

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Topics: cybersecurity

Organizations: Siemens

As of Monday night, Siemens has confirmed that 17 of its products are affected by CVE-2021-44228 and there are many more that are still being analyzed. The German industrial giant has started releasing patches and it has provided mitigation advice.

Schneider Electric has also released an advisory, but it’s still working on determining which of its products are affected. In the meantime, it has shared general mitigations to reduce the risk of attacks.

Read more at SecurityWeek

Siemens Energy HRSG Digital Twin Simulation Using NVIDIA Modulus and Omniverse

The Autonomous Factory of the Future by Siemens

Artificial intelligence optimally controls your plant

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Topics: energy consumption, reinforcement learning, machine learning, industrial control system

Organizations: Siemens

Until now, heating systems have mainly been controlled individually or via a building management system. Building management systems follow a preset temperature profile, meaning they always try to adhere to predefined target temperatures. The temperature in a conference room changes in response to environmental influences like sunlight or the number of people present. Simple (PI or PID) controllers are used to make constant adjustments so that the measured room temperature is as close to the target temperature values as possible.

We believe that the best alternative is learning a control strategy by means of reinforcement learning (RL). Reinforcement learning is a machine learning method that has no explicit (learning) objective. Instead, an “agent” with as complete a knowledge of the system state as possible learns the manipulated variable changes that maximize a “reward” function defined by humans. Using algorithms from reinforcement learning, the agent, meaning the control strategy, can be trained from both current and recorded system data. This requires measurements for the manipulated variable changes that have been carried out, for the (resulting) changes to the system state over time, and for the variables necessary for calculating the reward.

Read more at Siemens Ingenuity

Industrializing Additive Manufacturing by AI-based Quality Assurance

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Author: Axel Reitinger

Topics: additive manufacturing, quality assurance

Organizations: Siemens

At Siemens we are aiming to significantly improve quality assurance in Additive Manufacturing (AM) with industrial artificial intelligence and machine-learning to accelerate the time from prototype to industrialization as well as the efficiency in large-scale serial production.

Data of all print jobs are collected in a virtual private cloud (encrypted and secured by two-factor authentication), which facilitates the analysis and comparison across multiple print jobs and factory locations.

A profile of the severity scores of the final prototype can be used to define upper control limits for the serial production, which are then the basis for an automatic monitoring of the printing quality in the industrial phase. This could include, for example, the automatic creation of non-conformance reports (NCR).

The application calculates a severity score per printed part on the layer and additionally a severity score for the whole build plate. The severity score per part is calculated on the area of the bounding box of every single part, which helps to focus on those issues in the powder bed that can negatively impact the part’s quality. It allows a detailed monitoring of every part during the print process and is used by technical experts to evaluate if further Non-Destructive-Evaluation (NDE) of the finished part is required.

Read more at Siemens Ingenuity

SKF uses cloud to offer new business models

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Topics: predictive maintenance, business model innovation

Organizations: Siemens, SKF Group

The idea is simple: Instead of buying industrial bearings – whether for conveyor belts, pumps, crushers, paper machines, steel or pulp mills and railway bogies – SKF’s customers pay for uninterrupted rotation services. Under SKF’s Rotating Equipment Performance service, customers pay a fixed fee, which covers the provision of bearings, seals, lubrication and condition monitoring.

On the topic of payment: For many manufacturing operations, the argument for XaaS is that payments fall under operational expenditures (OPEX), thus leaving capital expenditure (CAPEX) budgets intact for the big, essential investments. When a contract is drawn up the parties agree on targets, which could be machine production level, uptime or other KPIs. Digitalization is essential for delivery and to ensure the promised uptime.

Aside from detecting failures before they happen, data evaluation is essential for selecting the right rotation services. SKF can measure the rotating equipment performance and from the data recognize whether the solution it has proposed is meeting its customers’ needs. If not, adjustments can be made to provide the best solution possible.

Read more at Siemens Blog

The Autonomous Factory: Innovation through Personalized Production at Scale

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Author: Dr Ralph-Christian Ohr

Topics: IIoT, digital twin, autonomous production

Organizations: Siemens

Personalized products are in high demand these days. Meeting this demand is leading companies to increasingly automate their production processes and even make parts of it autonomous. However, this approach presents a trade-off: with increasing personalization comes increasing complexity. Therefore, companies need to decide on the expedient extents and levels of automation to be implemented in their factories. Two strategies that may help along the way: 1. Limited implementation in selected areas. 2. Co-creation with trusted partners.

Read more at Siemens Ingenuity

Evolution of Machine Autonomy in Factory Transactions

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Author: Stephanie Neil

Topics: IIoT, blockchain

Organizations: Industrial Internet Consortium, IOTA Foundation, Siemens, IBM

So while we’ve not completely entered the age of the machine economy, defined as a network of smart, connected, and self-sufficient machines that are economically independent and can autonomously execute transactions within a market with little to no human intervention, we are getting close.

The building blocks to create the factory of the future are here, including the Internet of Things (IoT), artificial intelligence (AI), and blockchain. This trifecta of technology has the potential to disrupt the industrial space, but it needs to be connected with a few more things, such as digital twin technology, mobile robots, a standardized way for machines to communicate, and smart services, like sharing machine capacity in a distributed ecosystem.

“The biggest obstacle is culture,” said IIC’s Mellor. “The average age of the industrial plant is 19 years. These are huge investments that last for decades. The organizations that run these facilities are very cautious. Even a 0.5% chance of failure can cost millions of dollars.”

Read more at AutomationWorld

Vaccine production: Marburg has the right stuff

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Topics: manufacturing execution system

Vertical: Pharmaceutical

Organizations: Siemens, BioNTech

BioNTech manufactures BNT162b2 in collaboration with US pharmaceutical specialist Pfizer. The company has started manufacturing at the production site in Marburg, in the German state of Hesse. The plant there comes with an ultramodern production facility for recombinant proteins. The relevant expertise is also available, since BioNTech also acquired a highly qualified employee base along with the production facility, all of whom are experienced in developing new technologies.

The facility in Marburg had been producing influenza vaccines based on flu cell culture, then changed over to recombinant proteins for cancer treatments and now manufactures mRNA vaccine.

All the improvements at the Marburg plant are Industry 4.0-compatible. One of the challenges with the conversion was the fact that it involved switching from rigid to mobile production with many single-use components. At the same time, working with mRNA meant a higher clean room class than was previously required in the facility. Paper is now an avoidable “contamination factor” that doesn’t arise with digital production. That was the basis for opting for the Opcenter Execution Pharma solution from Siemens as the new MES. This solution enables complete paperless manufacturing and fully electronic batch recording.

Read more at Siemens Blog

Cloud-based app for micro-breweries

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Topics: IIoT

Vertical: Beverage

Organizations: Deacan, Siemens, KAIJU Beer

When the yeast consumes the sugar to produce alcohol: That’s when the flavour is developed. It’s when beer becomes beer. Australian craft brewers are passionate about brewing, not industrial operational technology, yet Leonie Wong and Rex Chen from the MindSphere team still managed to make the data work for them; they want to always land the perfect brew and waste not a single drop.

In this market, Deacam, an Australian original equipment manufacturer (OEM), which provides automated brewing equipment and solutions to microbreweries, was looking to differentiate itself. Leonie Wong, responsible for Vertical Sales for Food & Beverage for Siemens Australia, and Solution Architect Rex Chen met with Deacam and their customers, the microbreweries themselves.

Read more at Siemens Blog

Complex machine validations performed with multiphysics simulation

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Author: Rahul Garg

Topics: digital twin, materials science

Vertical: Machinery

Organizations: Siemens

When new materials and methods are applied to manufacturing, it increases product complexity. But the benefits can be significant: Products are now lighter, smaller and more easily customizable to meet consumer demands. Multiphysics simulations enable machine builders to explore the physical interactions complex products encounter, virtually. It tracks interactive data of product performance, safety and longevity.

Read more at Plant Engineering

A digital twin solved the risks associated with the 50m smart patching line made by Raute

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Author: Ville Paso

Topics: digital twin, machine design

Vertical: Wood

Organizations: Siemens, Raute

The project consists of a digital twin and virtual commissioning of the production line to secure the project delivery for the new designed machine sections (material infeed and baseplate removal) of a patching line. Different scenarios could be created with the digital twin to optimize the design (i.e. avoidance of mechanical collisions etc.) and validate the concept before manufacturing the real machine sections.

Read more at Siemens Ingenuity

Artificial Intelligence: Driving Digital Innovation and Industry 4.0

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Author: @ralph_ohr

Topics: AI, machine learning

Organizations: Siemens

Intelligent AI solutions can analyze high volumes of data generated by a factory to identify trends and patterns which can then be used to make manufacturing processes more efficient and reduce their energy consumption. Employing Digital Twin-enabled representations of a product and the associated process, AI is able to recognize whether the workpiece being manufactured meets quality requirements. This is how plants are constantly adapting to new circumstances and undergoing optimization with no need for operator input. New technologies are emerging in this application area, such as Reinforcement Learning – a topic that has not been deployed on a broad scale up to now. It can be used to automatically ascertain correlations between production parameters, product quality and process performance by learning through ‘trial-and-error’ – and thereby dynamically tuning the parameter values to optimize the overall process.

Read more at Siemens Ingenuity

How Augmented Reality Became a Serious Tool for Manufacturing

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Author: James R. Koelsch

Topics: augmented reality, IIoT

Organizations: Autodesk, AVEVA, Dassault Systemes, Emerson, Siemens

Making monsters appear in games like Pokémon Go is not the only application for augmented reality these days. Industry is using the technology too, harnessing CAD data for training workers, standardizing workflows, and enabling collaboration.

Read more at Automation World

Speeding the Adoption of Additive Manufacturing

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Author: Ashley Eckhoff

Topics: 3D printing, additive manufacturing

Vertical: Machinery

Organizations: Siemens

Additive manufacturing (AM), or 3D printing offers a number of potential innovations in product design, while its flexible manufacturing capabilities can support a distributed manufacturing model - helping to unlock new business potential. However, when companies begin to consider all that is needed to make additive a reality— such as generative design, part consolidation, and topology optimization—it becomes clear that the traditional ways of designing and manufacturing parts are falling away.

Read more at Manufacturing.net