Siemens
Software : Operational Technology : General
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
Increase manufacturing processes by 25% with AI, Opcenter and Retrocausual a Siemens Partner
Plan, build and execute the next generation of automated production lines
NavVis and Siemens Smart Infrastructure bring spatial digital twin capabilities to Siemens Building X
NavVis, an innovator in reality capture and digital factory solutions, and Siemens Smart Infrastructure have collaborated to integrate accurate as-is 3D data and an immersive 3D experience to Siemens’ latest scalable digital building platform Building X™. “We are excited to enable the Siemens Building X open platform with NavVis technology. We firmly believe that accurate as-is 3D data at scale and immersive interaction with this data is critically important to make the vision of smart buildings a reality,” says Dr. Felix Reinshagen, CEO and Co-Founder at NavVis.
☁️🧠 Automated Cloud-to-Edge Deployment of Industrial AI Models with Siemens Industrial Edge
Due to the sensitive nature of OT systems, a cloud-to-edge deployment can become a challenge. Specialized hardware devices are required, strict network protection is applied, and security policies are in place. Data can only be pulled by an intermediate factory IT system from where it can be deployed to the OT systems through highly controlled processes.
The following solution describes the “pull” deployment mechanism by using AWS services and Siemens Industrial AI software portfolio. The deployment process is enabled by three main components, the first of which is the Siemens AI Software Development Kit (AI SDK). After a model is created by a data scientist on Amazon SageMaker and stored in the SageMaker model registry, this SDK allows users to package a model in a format suitable for edge deployment using Siemens Industrial Edge. The second component, and the central connection between cloud and edge, is the Siemens AI Model Manager (AI MM). The third component is the Siemens AI Inference Server (AIIS), a specialized and hardened AI runtime environment running as a container on Siemens IEDs deployed on the shopfloor. The AIIS receives the packaged model from AI MM and is responsible to load, execute, and monitor ML models close to the production lines.
⛓️🧠 Multinationals turn to generative AI to manage supply chains
Navneet Kapoor, chief technology officer at Maersk, said “things have changed dramatically over the past year with the advent of generative AI”, which can be used to build chatbots and other software that generates responses to human prompts.
New supply chain laws in countries such as Germany, which require companies to monitor environmental and human rights issues in their supply chains, have driven interest and investment in the area.
Battery pack assembly line powered by Process Simulate software and the Industrial Metaverse
Microsoft Cloud for Manufacturing: Tackling data accessibility in manufacturing alongside partners
I’m very excited about all the updates being shared at Microsoft Inspire 2023, particularly about the announcement of the new AI Cloud Partner Program (MACPP) and the additional offerings and benefits this brings for partners. Under the MACPP, I’m thrilled to announce that we will be including manufacturing partner solutions through new independent software vendor (ISV) designations.
This designation represents our commitment to bringing the best partner solutions to our customers and provides a way for customers to identify proven partner solutions aligned with the Microsoft Cloud and our industry clouds. The designation validates that our partners’ solutions meet the high standards of data accessibility specific to the manufacturing industry.
Mapping of multimodality data for manufacturing analyses in automated fiber placement
Automated Fiber Placement (AFP) is an advanced composites manufacturing technique utilized for industrial scale structures. During this process, data is collected from a multitude of modalities spanning numerical analysis of processing parameters to inspection techniques. With data collection existing both in-situ and ex-situ. To ensure interoperability of multimodality data, a mapping is necessary to understand the relationship between these various conditions, and the positioning on the tool surface. This paper defines a mapping technique which enables the evaluation of spatial data from many different sources within the AFP process. Through these mapping techniques, a global array of data is generated that includes all aspects of AFP manufacturing. The developed methodologies are applied to the manufacturing of a doubly curved part. Results showcase the ability to map multimodality data into a uniform format. With the uniform format of the data, further steps can be made to the improvement of fiber paths and processing parameters.
Intrinsic and Siemens collaborate to accelerate the integration of AI-based robotics and automation technology
Intrinsic, an Alphabet company, and Siemens have teamed up to explore integrations and interfaces between Intrinsic’s robotics software, which is designed for easy use of AI-based capabilities, and Siemens Digital Industries with their open and interoperable portfolio for automating and operating industrial production.
📊 Data pools as the foundation for the smart buildings of the future
Today’s digital building technology generates a huge amount of data. So far, however, this data has only been used to a limited extent, primarily within hierarchical automation systems. Data however is key to the new generation of modern buildings, making them climate-neutral, energy- and resource-efficient, and at some point autonomous and self-maintaining.
More straightforward is the use of digital solutions for building management by planners, developers, owners, and operators of new buildings. The creation of a building twin must be defined and implemented as a BIM goal. At the heart of it is a Common Data Environment (CDE), a central digital repository where all relevant information about a building can be stored and shared already in the project phase. CDE is a part of the BIM process and enables collaboration and information exchange between the different stakeholders of the construction project.
Beyond the design and construction phases, a CDE can also in the operation phase help make building maintenance more effective by providing easy access to essential information about the building and its technical systems. If information about equipment, sensors, their location in the building, and all other relevant components is collected in a machine-readable form from the beginning of the lifecycle and updated continuously, building management tools can access this data directly during the operations phase, thus avoiding additional effort. The exact goal is to collect data without additional effort. To achieve this, in the future engineering and commissioning tools must automatically store their results in the common twin, making reengineering obsolete.
Accelerating the future of smart manufacturing with Deloitte and Siemens
World’s Leading Electronics Manufacturers Adopt NVIDIA Generative AI and Omniverse to Digitalize State-of-the-Art Factories
More than 50 manufacturing giants and industrial automation providers — including Foxconn Industrial Internet, Pegatron, Quanta, Siemens and Wistron — are implementing Metropolis for Factories, NVIDIA founder and CEO Jensen Huang announced during his keynote address at the COMPUTEX technology conference in Taipei.
Supported by an expansive partner network, the workflow helps manufacturers plan, build, operate and optimize their factories with an array of NVIDIA technologies. These include NVIDIA Omniverse™, which connects top computer-aided design apps, as well as APIs and cutting-edge frameworks for generative AI; the NVIDIA Isaac Sim™ application for simulating and testing robots; and the NVIDIA Metropolis vision AI framework, now enabled for automated optical inspection. NVIDIA Metropolis for Factories is a collection of factory automation workflows that enables industrial technology companies and manufacturers to develop, deploy and manage customized quality-control systems that offer a competitive advantage.
Instrumental joins Siemens Dynamo Program and will complement Siemens’ Teamcenter Quality offering with AI Capabilities
Instrumental, the leading AI-powered manufacturing quality platform, is excited to announce its official collaboration with Siemens through the Siemens Dynamo program, an open innovation program serving as a commercialization vehicle for start-up companies with Siemens, its customers and partners.
In the scope of the collaboration, Instrumental will integrate its cloud-based manufacturing AI platform with Siemens’ Teamcenter® Quality software from the Siemens Xcelerator portfolio. This combination will enable engineers leveraging Instrumental’s AI-powered insights to streamline problem-solving processes in Teamcenter Quality (e.g., 8D, Corrective and preventive action – CAPA). The Siemens closed loop quality approach, empowered by AI capabilities, extends the conventional quality cycle by connecting all data along the product lifecycle.
Enabling 3D Printing Automation with HP and Siemens
Siemens and Microsoft drive industrial productivity with generative artificial intelligence
Siemens and Microsoft are harnessing the collaborative power of generative artificial intelligence (AI) to help industrial companies drive innovation and efficiency across the design, engineering, manufacturing and operational lifecycle of products. To enhance cross-functional collaboration, the companies are integrating Siemens’ Teamcenter® software for product lifecycle management (PLM) with Microsoft’s collaboration platform Teams and the language models in Azure OpenAI Service as well as other Azure AI capabilities. At Hannover Messe, the two technology leaders will demonstrate how generative AI can enhance factory automation and operations through AI-powered software development, problem reporting and visual quality inspection.
Deloitte and Siemens Model-Based Enterprise: Now, Near, Far
MakerVerse raises €9.4 million to expand its on-demand manufacturing platform
Berlin-based MakerVerse has raised a €9.4 million Series A funding round to scale its AI-powered on-demand manufacturing supply chain platform. The round was led by 9.5 Ventures and all investors from the previous Seed round got involved again, including Siemens Energy and ZEISS.
MakerVerse will expand its “one-stop shop” concept for advanced manufacturing with more technologies and materials while advancing support to integrate the platform into customers’ existing systems.
DENSO reduce component simulation time by 80 percent using its Simcenter 3D and NX integrated process
A major challenge today is to improve productivity in the design and simulation of automotive parts. Even before the rise of software solutions, designers focused on geometry and turned to analysts to test and validate performance. However, simulation teams have always been much smaller than design teams – creating a bottleneck in the development process.
With Siemens tools, DENSO saw an opportunity to streamline the traditional workflow between design and engineering analysis, uniting the disciplines. This was particularly true for component design and analysis where simulation processes are more routine. DENSO’s goal was to reduce or eliminate the iteration with a new workflow.
AI and the chocolate factory
“After about 72 hours of training with the digital twin (on a standard computer; about 24 hours on computer clusters in the cloud), the AI is ready to control the real machine. That’s definitely much faster than humans developing these control algorithms,” Bischoff says. Using reinforcement learning, the AI has developed a solution strategy in which all the chocolate bars on the front conveyor belts are transported on as quickly as possible and the exact speed is only controlled on the last conveyor belt - is interestingly quite different from that of a conventional control system.
The researchers led by Martin Bischoff were able to make their approach even more practical by compressing and compiling the trained control models in such a way that they run cycle-synchronously on the Siemens Simatic controllers in real time. Thomas Menzel, who is responsible for the department Digital Machines and Innovation within the business segment Production Machines, sees great potential in the methodology of letting AI learn complex control tasks independently on the digital twin: “Under the name AI Motion Trainer, this method is now helping several co-creation partners to develop application-specific optimized controls in a much shorter time. Production machines are now no longer limited to tasks for which a PLC control program has already been developed but can realize all tasks that can be learned by AI. The integration with our SIMATIC portfolio makes the use of this technology particularly industry-grade.”
The Digital Twin of Wire Harness Manufacturing
SKF uses cloud to offer new business models
In production environments, there’s an alternative to owning resources and outsourcing: performance-based contracts. At SKF, for example, customers pay to use assets and benefit from guaranteed uptime. Effective delivery of Everything-as-a-Service (XaaS) business models depends on data collection and processing. On top of that, MindSphere, the leading industrial IoT as a service solution, as part of the Xcelerator portfolio brings quite a few more advantages.
The advantage of so-called Everything-as-a-Service (XaaS) business models is that companies pay for only what they use. Increasingly, XaaS is being extended to production assets. An example can be found with SKF, a manufacturer of, among others, rotating equipment like bearings. 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.
How Robotic Sewing Experiment Got Levi’s Attention
The teams’ early work integrated sewing machines with collaborative robot systems and designed an end effector capable of lifting and controlling a single large ply of fabric. Recent projects have built upon these developments to be able to robotically conduct more advanced operations like hemming, fabric fusing, pocket setting and curved stitches. The two firms then turned to Sewbo, a company that wants to address a common problem that prevents robotics from meshing with apparel production—the technology often has difficulty trying to handle limp, flexible or floppy fabrics, and thus can’t start the sewing process.
Because the machines are also expensive, according to Zornow, the upfront investment and maintenance costs are also high. To make matters tougher, the downtime can be substantial, he said. “Consequentially, you sort of find this paradigm where although a lot of the tools do exist, they’re not really getting used,” Zornow said. Rather than teach robots how to handle cloth, Sewbo temporarily stiffens the fabric with a nontoxic polymer, enabling off-the-shelf industrial robots to build garments from rigid cloth, just as if they were working with sheet metal. Zornow told Rivet that the use of the stiffening agent was the “big breakthrough” that made the technology innovation possible.
Accenture and Siemens: Tackling industrial machinery challenges through Intelligent Service and Asset Lifecycle (ISAL)
The industrial machinery industry is being transformed by global supply chain disruptions, changes to equipment practices and a push for greater sustainability. Executives seeking to adapt to the shifting landscape need reliable partners and world-class solutions.
Accenture and Siemens can partner with industrial machinery manufacturers to help manage this period of transformation. This post discusses trends in the sector, the Intelligent Service and Asset Lifecycle (ISAL) solution and the role Accenture and Siemens play to support a new direction for industrial machinery companies.
Comau and Siemens collaborate to integrate robotics and artificial intelligence in the PLC
SIMATIC Robot Library and the “Comau Next Generation Programming Platform” use Profinet’s “Standard Robot Command Interface,” a growing industrial communication protocol. Thanks to this standard, manufacturing companies can quickly and easily program and manage Comau robots using Siemens software and control systems. As the integration and automation between the Siemens PLC and the robotic controller do not require prior knowledge in robotic programming the solution reduces work time and costs, increasing production efficiency.
Siemens, Gecko Robotics Develop Ultrasonic Maintenance Robots
Siemens has announced a three-year collaboration with Gecko Robotics to develop and roll out ultrasonic robotic inspection services across Europe. The partners say the inspections will be a game-changer for the future of infrastructure inspections and maintenance across a range of industries such as power generation and the oil and gas sector. Under the collaboration, Gecko Robotics will provide its remote-controlled robots fitted with ultrasonic transducers, localization sensors, lasers and H cameras. The spider-like robots adhere to the surface of different equipment types, moving horizontally or vertically across the equipment while scanning it for any signs of wear and tear, with managers able to monitor corrosion trends over time and predict necessary maintenance.
Siemens and Desktop Metal begin partnership with aim of accelerating sustainable additive manufacturing at scale
Siemens and Desktop Metal have announced a multi-faceted partnership aimed at accelerating the adoption of additive manufacturing for production applications with a focus on the world’s largest manufacturers.
The collaboration will touch multiple aspects of the Desktop Metal business. This includes increased integration of Siemens technology in Desktop Metal’s AM 2.0 systems, including operational technology, information technology and automation. Desktop Metal says its solutions will be fully integrated into Siemens simulation and planning tools for machine and factory design. Siemens Digital Twin tools will now be used for designing certain machines, and Siemens Advanta can simulate all levels of the binder jetting process and global plant planning, which Siemens says enables fast and reliable decisions for factory planning.
Estonia’s Skeleton to invest €220 million to build supercapacitor factory in Leipzig
Estonian energy storage tech firm Skeleton Technologies will invest €220 million to build a fully automated, digitalised manufacturing plant to produce supercapacitors at Leipzig in Germany. Out of the investment, €100 million will be utilised in manufacturing equipment in Leipzig area and €120 million will be used for scale-up and R&D. Planned by Siemens, the production at the factory is slated to begin in 2024, Skeleton said. The collaboration will help scale up the production of next-generation supercapacitors. The factory in Markranstaedt, Leipzig will build 12 million cells a year, 8 million of which would be smaller cells for passenger vehicles and 4 million would be larger cells for heavy-duty transportation.
The Metaverse Goes Industrial: Siemens, NVIDIA Extend Partnership to Bring Digital Twins Within Easy Reach
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.
Siemens acquires Brightly Software to accelerate growth in digital building operations
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.
Siemens buys UK industrial IoT firm Senseye for global smart factory push
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.
Robots Become More Useful In Factories
“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.”
Digital twins improve real-life manufacturing
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.
Industrial Organizations Targeted in Log4Shell Attacks
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.
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
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.
Industrializing Additive Manufacturing by AI-based Quality Assurance
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.
SKF uses cloud to offer new business models
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.
The Autonomous Factory: Innovation through Personalized Production at Scale
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.
Evolution of Machine Autonomy in Factory Transactions
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.”
Vaccine production: Marburg has the right stuff
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.
Cloud-based app for micro-breweries
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.
Complex machine validations performed with multiphysics simulation
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.
A digital twin solved the risks associated with the 50m smart patching line made by 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.
Artificial Intelligence: Driving Digital Innovation and Industry 4.0
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.
How Augmented Reality Became a Serious Tool for Manufacturing
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.
Speeding the Adoption of Additive Manufacturing
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.