Mar 25, 2025

How sustainability in production delivers measurable results

• #circulareconomy
• #industrie40
• #sustainability
• #twintransition

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Susanne Weller

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Companies that make their manufacturing operations sustainable reap economic benefits: they cut costs through energy-efficient processes and demand-driven resource planning, reduce waste and emissions, and comply with regulatory requirements. At the same time, they position themselves as leaders for customers and partners who also focus on sustainability. The results of a strategy that is both sustainable and digital are most visible in manufacturing and production, where the metrics that demonstrate the impact of sustainable and digital efforts are created. Two cases show what software-defined automation and digital twins can do for battery recycling and machine manufacturing, respectively.

Regardless of whether companies are required by law to make their business more sustainable, or whether they want to align ecology, economy, and social issues out of conviction or strategic considerations: The long-term opportunities and benefits lie primarily in these areas:

1. Use resources efficiently
2. Saving energy
3. Reduce production cost

Achieving circularity in production

Those who want to align their production even more closely with the principles of the circular economy go one step further. The focus then shifts to achieving circularity in production. The first step is to use life cycle assessments to understand what resources are used when and where in production, and to know the energy and material consumption of individual production units. The next step is to create the conditions for closed loops: energy is sourced from renewable sources, products can be reused or recycled at the end of their life cycle, and production processes generate less waste that can be reintroduced into production. A key component is product design that ensures materials and components can be easily separated, repaired, or reused to build recyclability into the design phase. These approaches not only help reduce the environmental footprint, but also create long-term economic benefits by conserving resources and securing raw materials.

However, challenges such as high investment costs and adapting existing supply chains need to be considered. At the same time, socially sustainable approaches, such as supporting local employment, offer additional potential. By combining proven processes with new technologies such as additive manufacturing or targeted retrofits, companies can implement circular production methods more efficiently.

Combining digitization and sustainability

Strategy is one thing. Implementing it successfully requires not only investment and innovation, but also advanced technologies that make processes more efficient and measurable. In addition, the action plans resulting from the strategy must be implemented in such a way that they deliver measurable results. Digitalization therefore plays a crucial role in the implementation of sustainability measures in production. It provides the technologies, such as digital twins, automation solutions, or artificial intelligence (AI), and the data that can be used to measure whether the implementation of the measures is successful both ecologically and economically. In particular, digital twins can help to virtually simulate and optimize production processes, while automation and AI enable precise control and adjustment of resource utilization. The parallel transformation towards a digitalized and sustainable production method is referred to as the “twin transition”. It is considered the key to the success of change processes.

Achieving sustainability with data and technology

12.7 million tons of CO2 can be saved in industry by 2030 through accelerated digitization, according to the study “Klimaeffekte der Digitalisierung 2.0” by Bitkom e.V. (German only). According to the study’s findings, two technologies are crucial for this. The first is production automation. It ensures that processes in networked systems, machines, workpieces and components run independently and with the lowest possible use of materials and energy. The other is digital twins, virtual representations of real processes and machines. They allow innovations or changes and adaptations to processes and systems to be implemented and optimized digitally before they are transferred to the real production world. This saves material, energy and labor.

Two use cases show what examples already exist in practice.

Example 1: Up to 29.5 percent energy savings in machine tool manufacturing with digital twins

Machine tools play a central role in manufacturing companies. To change their shape, machining – turning, milling, drilling and grinding – is an important step in production. Cooling lubricants are often used in this process. But their use comes at a high energy cost. So high, in fact, that a consortium of research and industry partners – including us – have joined forces to develop measures to reduce energy consumption and increase productivity in the E-KISS* project, which is funded by the German Federal Ministry of Economics and Technology (BMWK).

E-KISS combines various technologies such as data science, digital twins and retrofit measures. The role of the digital twin was to map the complete digital picture of the real systems, i.e. the plant with its properties, states, dependencies and the behavior of the systems among each other. With the focus and objective of “achieving energy savings in the coolant supply”, it was particularly important to capture data on processes, tools and workpieces and to continuously record changes. Through this data-based modeling of the system, we developed the basis for approaches and methods to optimize energy consumption in the research network.

The result at the end of the project was impressive: the machine tool coolant production facility consumed 13,590 kilowatt-hours less per year, a 29.5 percent reduction in electricity. This was only possible thanks to the interplay of sustainable goals, retrofit measures that affected individual components, data science to collect and interpret the data, and the digital twin as a key technology for analyzing and optimizing processes.

Read the full use case here: How digital twins help save up to 29.5 percent energy in mechanical engineering

Example 2: Digital twins optimize battery recycling

The future of mobility is electric. Many electronic vehicles rely on lithium-ion batteries. However, the lifecycle of batteries has a significant impact on the environment, from the extraction of raw materials to the energy used in production and disposal. Battery recycling sounds like a good solution, but in reality it often fails: there are many battery manufacturers, a wide range of different battery packs, and no single raw material is used in the individual batteries. As part of the ZIRKEL* research project, funded by the German Federal Ministry of Education and Research (BMBF), an interdisciplinary consortium from industry and research is investigating how automation solutions can help disassemble batteries on a large scale. The goal of ZIRKEL is to improve the ecological footprint of batteries. At the same time, the productivity and efficiency of the dismantling, separation and cutting processes for battery systems and electric motors are to be sustainably increased. To carry out these processes, Liebherr Verzahntechnik built a system whose digital image we created using our Digital Twins.

The result: the digital twins were able to capture the real processes, determine the optimal recycling path through data analysis, determine and optimize the material cycles, and contextualize the data for AI-based analysis. The data was stored in a database to ensure the traceability of the recycled components for the EU Battery Pass. Acting as a data hub, the digital twins orchestrate a seamless flow of information between all stakeholders, increasing process efficiency, flexibility, and cost-effectiveness.

In short, the digital twins promote a sustainable circular economy in the field of electromobility.

More about the project: Circular Economy: The crucial role of digital twins from Ascon Systems in battery recycling

The BMBF will present the results of ZIRKEL at the Hannover Messe 2025 (March 31 to April 4, 2025, Hall 2, Stand A35).

*E-Kiss funding code: 03EN2037B; the E-KISS project partners include (besides us): Robert Bosch GmbH, ONLINE Industrieelektrik und Anlagentechnik GmbH (Online IAT), Technische Universität Braunschweig (IWF).

*ZIRKEL funding code: 02J21E044; Zu den beteiligten Partnern des Projekts ZIRKEL gehören neben dem BMBF und uns unter anderem auch In addition to the BMBF and ourselves, the partners involved in the ZIRKEL project include Liebherr-Verzahntechnik, DMG Mori, Institute for Particle Technology at TU Braunschweig, Fraunhofer Institute for Surface Engineering and Thin Films, Fraunhofer Institute for Machine Tools and Forming Technology, Synergeticon.

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Jan 13, 2025

AI in Production: How to Learn from Process Data

• #innovation
• #ITgoesOT
• #manufacturing
• #resilienteproduktion
• #resilientproduction
• #sdm
• #softwaredefinedmanufacturing
• #standards

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Susanne Weller

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Artificial Intelligence (AI) in manufacturing surpasses traditional approaches to process optimization. It analyzes and learns faster, provides more and better information for decision-making, and reveals new possibilities for action. This makes AI a driver for improved efficiency, quality, and flexibility across all areas. However, these successes don’t come automatically just by implementing AI. The foundation lies in meaningful process data. These data enable the required complex analyses that lead to new insights and optimization approaches. But how do you obtain these data, and how can their quality be assessed? Which AI method is best suited for specific applications? Marcus Röper, AI expert and Product Owner for Embedded AI at Ascon Systems, explains how it all fits together.

When it comes to monitoring, analyzing, and optimizing complex production processes in real time, AI becomes a game-changer. Especially when combined with digital twins and automation technologies, it creates a powerful ecosystem for continuous adjustments, precise forecasting, and integrative process orchestration. For AI applications to fully realize their potential, they require a specific quality of process data. In many cases, this quality is not inherently available and must first be established.

Process Data as the Foundation for AI Applications

Process data are generated during production or manufacturing processes. They provide information about the condition, changes in condition, and performance of machines and processes. Their sources include machine and equipment sensors, IoT devices, or control software such as ERP, MES, and SCADA systems. AI applications are built upon this process data.

Five key factors are particularly important for evaluating the reliability and usability of data:

1. Data Quality: The quality of process data is crucial to the success of AI applications. High data quality means that the data is accurate, reliable, and free from errors or outliers. Inaccurate or flawed data can lead to incorrect analyses and inefficient—or even harmful—decisions.
2. Data Completeness: Complete data ensures that all relevant information for the process is captured and available. Missing data creates gaps in the analysis, reducing the performance of AI models.
3. Data Consistency and Standardization: Consistent and standardized data formats facilitate the integration and processing of data from various sources. Uniformly structured data allows AI algorithms to operate more efficiently and simplifies the interpretation of results.
4. Data Timeliness and Real-Time Availability: Up-to-date process data is critical for monitoring. It forms the basis for timely or even real-time decisions. AI applications can thus respond immediately to process changes and recommend proactive measures.
5. Data Security and Privacy: Protecting process data from unauthorized access and complying with data protection regulations are essential. Security measures for external access ensure the integrity and confidentiality of the data, while internal privacy policies ensure that sensitive information is handled in accordance with legal requirements and internal compliance rules.

When these factors are considered, and the data is properly prepared, it provides a strong foundation for AI models to build upon and operate effectively. The next question is how to turn this into actionable knowledge.

Process Intelligence with AI: Learning from Process Data

The ability of AI to learn from process data means it analyzes insights derived from the data and combines them with its knowledge of optimal conditions and understanding of complex workflows. It identifies patterns, can recommend decisions, and generates forecasts and predictions. These insights help companies accurately predict future events such as demand, bottlenecks, or failures. AI also assists in classifying and quickly interpreting data, which can significantly improve the foundation for business decision-making.

Approaches like Process Mining and Data Mining combine the analysis of process workflows with the search for previously hidden correlations in large datasets. Modern Large Process Models take this a step further by fully modeling and simulating extremely complex, dynamic processes. This enables real-time optimization of process flows, as well as improvements in resource utilization and capacity planning. The choice of the right AI method is also critical for successful process optimization. 

AI Methods for Process Optimization

AI methods encompass a wide range of technologies, including generative algorithms such as Large Language Models (LLMs) like GPT, as well as specialized algorithms from fields like Machine Learning (ML), Deep Learning (DL), and Reinforcement Learning (RL).

The choice of AI method depends on the specific use case. LLMs are excellent for text-based applications and knowledge management, while ML and DL excel in optimization and prediction when working with large, complex datasets. By leveraging an ensemble of these technologies, companies can generate comprehensive and precise insights that enable efficient and sustainable process optimization.

Each method has specific strengths and is applied in different ways. Here is an overview of relevant AI approaches and the results they can deliver:

• Large Language Models (LLMs): Process natural language for production instructions and reports, enhance communication and decision-making through efficient analysis, and extract relevant information from large text datasets.
• Outlier Detection: Identifies anomalous data points, detects issues like machine faults and quality problems early, increases process stability, and reduces downtime.
• Classifications and Regressions: Classification algorithms categorize data, while regression models quantify relationships for numerical predictions. Both methods support informed decision-making by recognizing patterns and trends.
• Reinforcement Learning Algorithms: Learn from feedback from their environment and continuously optimize their strategies, enabling dynamic adaptation and autonomous process optimization.
• Time Series Forecasting: Analyzes historical data to identify trends and patterns, enables accurate predictions for production demands and maintenance cycles, and improves planning and resource allocation.

There are different providers offering these methods, often with significant variations. Before implementing AI, companies should evaluate their specific requirements using a checklist to determine which AI model or combination of models meets their needs. These tailored systems and methods must not only be effective but also transparent and explainable to function properly in the complex manufacturing environment. The methods can be used individually or in combination. It is also important to note that a single model is never a standalone solution in the context of process automation; rather, it serves as an intelligent core within a broader framework. 

Combined Measures for Data Integration and Transparency

To address the challenges of fragmented and isolated data, both technical and strategic measures are required. Data integration technologies such as ETL processes (Extract, Transform, Load), data catalogs, and data meshes play a central role in breaking down data silos and providing a holistic view of operations. Data meshes enable a decentralized data architecture, treating data as a product and assigning domain-specific teams’ responsibility for data processing and provisioning. Additionally, middleware for data integration ensures seamless interoperability between various systems.

Three Use Cases of AI in Practice 

1. Quality Control and Process Agility:

A company in the automotive industry employs AI-based image processing for visual inspection of components. By using computer vision algorithms, defects and quality deviations are detected in real time. The software-defined production process integrates these AI insights directly into the production line, enabling immediate adjustments. The result is a reduction in waste and production costs, along with improved precision and efficiency.

2. Anomaly Detection and Safety:

In the chemical industry, a company leverages AI to detect anomalies in real time, preventing production disruptions and accidents. The AI continuously analyzes sensor data and identifies nonlinear patterns that may indicate potential hazards, such as leaks or pressure spikes. Software-defined production allows the immediate implementation of countermeasures proposed by the AI, enhancing operational safety and preventing environmental damage.

3. Prescriptive Maintenance and Self-Healing:
A machinery manufacturing company has implemented AI-powered predictive maintenance to minimize unplanned downtime. Sensor data from machines and equipment are analyzed to detect early signs of failure and wear. By integrating dynamic maintenance strategies, the AI accounts for specific aging and degradation behaviors. Software-defined production enables maintenance actions to be seamlessly incorporated into production schedules, initiating self-healing processes when necessary. This approach results in greater system reliability and significant cost savings.

The Future of Process Optimization with AI

Looking to the future, the next stage of process optimization with AI lies in the holistic analysis and optimization of complex systems. These systems not only include individual processes but also their interactions with other processes, resources, and external conditions. By leveraging AI, companies can simulate, analyze, and optimize their entire value chain to enhance efficiency, sustainability, and resilience. In addition to AI, other technologies such as digital twins, advanced simulation models, and data-driven decision-making mechanisms are also utilized. These enable the modeling and optimization of complex scenarios and dependencies.

This interplay of AI, data, and intelligent analysis represents a paradigm shift in process optimization. It allows companies to implement localized process improvements while also developing system-wide strategies aimed at long-term sustainability and competitiveness. 

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Nov 4, 2024

Digital Twins in the process industry: fluidics specialist Bürkert expands collaboration with Ascon Systems

Press Release

• digitalezwillinge
• Effizienz
• prozessindustrie

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Susanne Weller

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Stuttgart / Ingelfingen, November 4, 2024 – Bürkert Fluid Control Systems, a leading global supplier of measurement and control technology for fluids and gases, has been working with software-defined solutions from Ascon Systems, a deep-tech specialist for process automation, since 2021. Digital twins from Ascon Systems are now also being used at Bürkert. They increase flexibility and efficiency in various applications of Bürkert’s customers because they store, provide and reuse process data in a contextualised manner independently of hardware, therefore optimising the control of the often complex fluidics technology. At the SPS trade fair in Nuremberg (12 to 14 November 2024), Bürkert and Ascon Systems will demonstrate the interaction of the technologies and show the potential.

Fluidics technology is precision work. It is used as a key technology in many sectors of industry whenever the flow of liquids or gases needs to be controlled. Applications can be found in the pharmaceutical, chemical, consumer goods and food industries and are increasingly common in areas of green engineering, such as fuel cell technology or bioreactors. The challenge in these systems is to deliver the exact amount of fluid in a precisely defined time and speed. Both the hardware, in the form of sensors, actuators and pumps, and the software must be able to meet these high standards of precision. 

Bürkert is one of the world’s leading suppliers capable of providing such precision technology. In cooperation with Ascon Systems, Bürkert has so far transferred duties from hardware to software – in other words, IT goes OT. This has already enabled Bürkert to create a hardware-independent description of functions, model data flows and network data across systems and locations. 

The two companies are now expanding their collaboration and taking the next step in software-defined production with the introduction of digital twins. The digital twins from Ascon Systems enable Bürkert to store, provide and reuse data in a contextualised manner. This means Bürkert can help its customers to configure and control applications. This enables them to leverage software to make hardware usable for multiple applications and to implement changing requirements, such as those arising from demand, product changes or new product launches, more quickly. This has a positive effect on time-to-market, while also increasing sustainability and improving the CO₂ footprint Bürkert and Ascon Systems are now demonstrating what this high-tech interplay can look like at SPS in Nuremberg (details below). 

Dr. Anne März, Head of Digital Tool Chain at Bürkert Fluid Control Systems, says: “Digital twins are becoming increasingly popular for optimising production processes. Now, they are also becoming a reality in fluid technology. Ascon Systems technology helps to significantly simplify the creation and scaling of digital twins and their networking to the real world. This approach enables us to realise our customers’ requirements faster and more flexibly. Customers can think about and describe the desired application from their own perspective, while we translate it into a hardware-neutral solution using digital twin technology. Once a solution has been found, it is transferred to the real world and can be replicated for other locations and customised with other components on the hardware side. By expanding our collaboration with Ascon Systems, we are now in a position to rethink digital solutions. We also see great potential in this for the future.”

Dr. Anne März, Head of Digital Tool Chain, Bürkert Fluid Control Systems

Jens Mueller, CEO from Ascon Systems, adds: “Since we started establishing digital twins in industrial applications in 2017, the possibilities for deployment scenarios and applications in all industries have increased enormously. We are delighted that we can now extend our long-standing, trusting cooperation with Bürkert to the area of digital twins. Together, we can show how software-defined precision technology can lead to greater efficiency, sustainability and cost reductions in hardware too. We believe this transformation is one of the most important opportunities for companies to position themselves resiliently for the future in global competition.” 

Jens Mueller, CEO, Ascon Systems

Ascon Systems and Bürkert at SPS in Nuremberg: 

The SPS (Smart Production Solutions) trade fair will take place in Nuremberg from 12 to 14 November 2024. Ascon Systems and Bürkert will each be represented with a stand. You are cordially invited to come by for a demonstration of the joint technology.  

To make an appointment, please contact 

Jürgen van Santen, +49 7940 10 91 302, juergen.vansanten@burkert.com

Michael Polaczy, +49 151 729 675 28, michael.polaczy@ascon-systems.de  

Ascon Systems, Hall 6, Stand 117

Presentation “The factory of the future is more than virtualization”: Jens Mueller, CEO Ascon Systems

• Tuesday, 12 November 2024, 11:20 to 11:40 Uhr, Hall 6, Forum 

Bürkert Fluid Control Systems, Hall 7, Stand 360

Example concept “Virtual calibration: how digital twins can fundamentally change processes in the future.”

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Aug 8, 2024

The future of manufacturing: How digital twins are driving Industry 4.0 forward

Digital twins digitally represent real or future physical objects and processes. They help companies to increase their added value and produce more efficiently, cost-effectively and sustainably. However, their potential – especially in software-defined manufacturing, which many companies are striving for – is often not yet fully exploited.

• #digitaltwin
• #IndustrialMetaverse
• #industry40
• #innovation

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Susanne Weller

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Faster product launches, increased flexibility in production, increased independence from skilled workers and resources as well as faster response options and adjustments to market changes: These added values are leading to digital twins becoming established in more and more business processes in industry, particularly in manufacturing, and accelerating the digital transformation. Digital twins are also a key technology and the heart of the industrial metaverse, which is playing an ever more important role in the competitiveness of companies in Germany. Innovations, expanded cooperation, integration and new business areas.

Digital twins are currently used in manufacturing, particularly in remote monitoring, production monitoring and optimization, predictive maintenance, quality control, product development and product design. Within these value chains, they enable continuous analysis of the production line. This allows companies to quickly identify and eliminate bottlenecks and inefficiencies as well as deviations from quality, detect potential failures at an early stage and take maintenance measures before production stops.

In April 2023, the digital association Bitkom e.V. published the results of a survey of 603 companies on digital twins (in German only). According to the results, 66% of the companies surveyed are already using digital twins, are planning to do so or can imagine doing so in principle. According to Bitkom, 63% of the industrial companies surveyed are also of the opinion that “digital twins are indispensable in order to survive in international competition”. Various application scenarios show why the importance of digital twins will continue to increase in the future and what contribution they can make to business success.

IoT and AI are driving the evolution of digital twins

The concept of the digital twin has evolved considerably in recent years. While digital twins were originally just digital models and copies of real states, today they are dynamic, data-driven models that enable real-time monitoring, simulation and optimization of production and products. They can – but do not have to – be used together with the asset administration shell (AAS), which ensures the necessary interoperability in industrial processes. The drivers behind the evolution of digital twins are, on the one hand, the pressure of international competition, which places greater demands on flexible and highly efficient production. On the other hand, advances in technology and infrastructure are paving the way for further developments and innovations.

The Internet of Things (IoT) is leading to an increasing spread and improvement of technologies for data collection and networking of machines with each other and with communication devices such as tablets and smartphones. The data is displayed on cloud-based platforms. Equipping machines with communication-capable sensors has increased the number of devices and processes that send and receive data, as well as the quantity and quality of data available to digital twins. Digital twins also benefit from artificial intelligence (AI) and machine learning. These technologies allow “big data”, i.e. large volumes of data, to be evaluated efficiently. They form the basis for precise predictions and optimization of processes and machines.

Application scenarios for digital twins in the future

Digital twins are an important building block in software-defined manufacturing (SDM). They can already be used in various new manufacturing applications and will become increasingly useful in the future. This is shown by these examples:

1. Control and monitoring of autonomous production systems

Digital twins play a central role in the control and monitoring of autonomous production systems. The digital twin mirrors and collects the current data of the machines and processes, monitors operation in real time, detects anomalies and deviations and makes suggestions for optimization that can be implemented in reality and have an impact on reality. For example, if a machine shows signs of wear, the digital twin can recommend preventive maintenance and also carry it out directly by adjusting the production plan accordingly. The digital twin acts as a control center that coordinates and controls the robots, machines and processes in real time.

2. More sustainability and better energy and resource management

Digital twins are used to optimize energy consumption and manage resources. They can simulate various scenarios and indicate where energy requirements can be sensibly reduced and resources used efficiently. This leads to three major benefits: lower energy costs, more sustainable production through reduced environmental and waste impact and optimized use of resources. Read more: How digital twins make the circular economy go round

3. Innovations, expanded cooperation, integration and new business areas

Digital twins enable seamless integration of supply chains, suppliers and partners. This promotes collaboration and coordination across company boundaries. The benefits lie in improved supply chain efficiency, increased transparency and traceability. The simulation and analysis of production processes and products leads to shorter development cycles. New ideas and technologies can be tested and optimized in the digital twin without risk and without using material before they are transferred to real production. This enables companies to bring new products to market faster. The ability to test and simulate processes and products virtually promotes a company’s ability to innovate.

4. Tailor-made, customer-specific production

Digital twins can change both production and the product. In production, they enable flexible adaptation to changing production requirements. Companies can react more quickly to market changes and adapt their production lines accordingly. Software-centered production is possible with digital twins. This means that product variations can be implemented at software level instead of hardware level and can therefore be scaled at any time. New or different customer wishes and requirements can be integrated into the digital twin at any time and customized products can be developed and produced in real time.

5. Digital twins at the heart of the Industrial Metaverse

In the Industrial Metaverse, digital twins enable an immersive and interactive environment in which physical and virtual worlds merge. This promotes innovation and leads to improved efficiency and productivity. Digital twins are at the heart of the Industrial Metaverse precisely because they provide a precise and dynamic digital representation of physical objects, systems and processes, enabling simulations and bidirectionality.

From the field: Better software-defined manufacturing

There are different digital twins. We use bidirectional digital twins. They are able to receive data in real time and can send data back to reality. This approach accelerates the transformation to software-defined manufacturing: The hard-coded store floor is being transformed into a software-defined one, and the classic automation pyramid is being replaced by intelligent, capability-based production modules. By using networked microservices, we control production and replace the traditional PLC. Digital twins provide a real-time representation of production systems and processes, enabling informed decisions based on current data. This allows us to offer value stream control for each individual product – something that is often not possible with traditional technologies.  

Two examples: Together with a research association, we have shown how digital twins help save up to 29.5% energy in mechanical engineering if the dosing of cooling lubricants were based on a digital twin.  The project also shows the potential for optimization through the combination of various measures, such as retrofitting, digital twins and data science.  

We successfully introduced digital twins at our customer BMW’s Innovation Hub in Dingolfing (Bavaria) (detailed description: How BMW is testing the factory control of the future with digital twins). The main focus here was on optimizing control and monitoring. An arrangement on a test track shows the interaction of the real store floor with digital twins, which fulfill several functions: They are executed directly as IT services that control the system, they interact with each other and influence production and they provide comprehensive company-wide information on production and logistics processes and product realization.  

Despite all the advantages and the wide range of applications of digital twins, many companies often still don’t know how to get started with the topic. We have therefore developed a multi-stage digital twin readiness check. This makes it possible to quickly identify how the company’s own infrastructure is set up, how it should be optimized with regard to the integration of digital twins and which strategy a roadmap for implementation can be based on. Find out more: https://ascon-systems.de/aim

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May 14, 2024

How digital twins make the circular economy go round

The European Green Deal aims to establish the first climate-neutral continent by 2050. A core element of the plan involves transforming Europe into a circular economy. This means employing a systematic approach that reduces waste while also conserving and reusing resources and raw materials. The envisioned circularity relies heavily on digital technologies such as digital twins. Digital twins enable transparency and efficiency and accelerate the transformation to a circular economy.

• #circulareconomy
• #digitaltwin
• #twintransition

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Susanne Weller

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The circular economy in industry

The circular economy has evolved from the linear economy and its principle of “take, make, and waste”. In the circular model, when businesses acquire and use resources to manufacture products, they don’t simply dispose of them after initial use, but rather return the resources to the value chain. These secondary materials can then continue to help generate value. This allows industrial enterprises to reduce expenditure on materials and create new sources of income by reusing, repairing, or recycling products and materials. The circular economy holds great economic potential for Germany according to a study from Deloitte and the Federation of German Industries (BDI) titled “Zirkuläre Wirtschaft: Herausforderungen und Chancen für den Industriestandort Deutschland” (The Circular Economy: Challenges and Opportunities for Germany as a Manufacturing Hub) in May 2021:

• The substitutability of important raw materials such as aluminum and steel and the transition to domestic secondary raw materials could boost the annual economic output of German industry by around €12 billion as well as add 180,000 new jobs.
• The circular economy promotes new business models and drives innovation mainly in three areas: reducing the amount of material used to produce goods, maximizing the amount of use obtained from materials (longevity), and recycling.

Like any transformation, moving to a circular economy also presents challenges to businesses, particularly with regard to changes in production activities. The opportunities here lie in digitalization — and this is where the digital twin comes into play. 

Digital twins as part of the twin transition

The twin transition, which describes a simultaneous digital and economic transition, is driving the implementation of the circular economy. Digital technologies provide the data and methods enterprises need to optimize processes, use resources more efficiently, and reduce the negative environmental impacts of their business activities. Digital twins — virtual representations of physical objects or systems — play a crucial role here.

They can help with incorporating the entire lifecycle of a product, planning effectively from product design all the way to end-of-life, and returning the components to the value chain. Digital twins, whether they are product or production twins, are essential for the implementation of a circular economy, mainly in regard to these six factors:

1. Optimizing product designs: With digital twins, companies can simulate products and processes virtually and eliminate the need to construct a physical prototype. This allows them to explore product design scenarios, apply AI copilots, and develop and expand a knowledge base of raw materials used, which is essential for end-of-life reuse.
2. Managing resources efficiently: Digital twins can provide precise data on the status and use of resources and materials. This information is critical for planning efficient recycling processes and enabling the reuse of components and materials. It thus supports the underlying principle of the circular economy, which dictates that materials be kept in the value chain as long as possible.
3. Saving energy: Optimized processes in operations, lower material use, and improved machine utilization all contribute to saving energy. Digital twins measure energy consumption with great precision and can accurately attribute it to the equipment or systems that consumed the energy. Detailed insights into energy flows in a factory become possible. This helps manufacturers smooth out energy consumption peaks while reducing total energy consumption.
4. Making forecasts and performing maintenance: By continually monitoring machines and processes, digital twins can predict when maintenance or repairs are necessary for machines and equipment. This helps to reduce downtime and extend the useful life of the equipment. And in a circular economy, it helps to reduce waste.
5. Establishing transparency, access to information, and traceability: Digital twins offer complete transparency across the entire lifecycle of a product. This transparency makes it possible to trace exactly where and how resources are being used. It also ensures that they can be effectively recycled or reused at end-of-life. This is especially important since many companies will soon be legally required to comply with documentation obligations such as the battery passport.
6. Supporting innovation and development of business models: Digital twins can support innovative business models such as Products as a Service. Business models like these focus on product use instead of ownership. They require accurate data in order to manage things like maintenance, returns, and recycling.

As a result of these advantages along many stages of the product lifecycle, digital twins play a crucial role in implementing and scaling the circular economy. Companies can not only act in a more environmentally friendly manner, but also become more efficient and competitive with digital twins. A real-life example shows how digital twins interact with other technologies and enable greater efficiency.

Use case: meeting the challenge of battery recycling

The global battery market is growing rapidly: Global demand for lithium-ion and sodium ion batteries will be 18.55 times as high in 2030 as it was in 2020 according to the study “Battery Monitor 2023” by the consultancy Roland Berger and RWTH Aachen University. Batteries are essential for the future of electric mobility. Until now, however, their ecological footprint has been problematic with respect to raw materials returning to the circular loop because not all materials are being recycled at end-of-life. The European Parliament therefore decided that starting in February 2027, all new LV batteries, industrial batteries, and batteries for electric vehicles with a capacity greater than 2 kWh will require a digital battery passport. This digital product pass will document the entire lifecycle of the battery including raw materials, use, and reuse.

One of the challenges for battery makers is disassembling the batteries at end-of-life so they can recycle them and return the materials to the resource loop. We participated in the ZIRKEL research project which examined how automation solutions can contribute to disassembling these batteries at scale as part of an interdisciplinary consortium of industrial and research entities that includes Volkswagen AG, Liebherr-Verzahntechnik, and Deckel Maho Pfronten. Our digital twins played an essential role in the project. They enabled in-depth analysis, definition, and modeling of data interfaces for the machines and AI platforms connected to them.

The contextualized data gained in this manner can then be used for AI analyses or in a blockchain to ensure the traceability of recycled components for the EU battery passport. During the disassembly and recycling processes, digital twins can adapt to different battery types and battery conditions, making it possible to complete material loops. They increase the efficiency of processes while enhancing flexibility and reducing costs. Digital twins thus support the sustainable circular economy in the field of electric mobility.

Conclusion

Implementing digital twins in the circular economy holds enormous potential for making industrial processes more efficient and sustainable. In combination with the European Green Deal, the technology promotes environmental conservation and makes a contribution towards economic resiliency, particularly in growth markets. For a successful transformation, it is therefore important for companies to recognize and capitalize on the opportunities that digital twins represent for implementing a holistic circular economy.

Read more about the ZIRKEL research project.

Find out more about Digital Twins and Ascon Qube.

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Apr 18, 2024

Ascon Systems Named Highly Commended NVIDIA Partner in the Category Industry Innovation

Ascon Systems has for the first time been honored as a Highly Commended NVIDIA Partner in EMEA, an annual award presented by the NVIDIA Partner Network (NPN) for exceptional contributions by its partners. The recognition was given for Industry Innovation, a category created to recognize the achievements of partners in a specific industry or field that represent outstanding contributions to business transformation. The NPN presents a total of 11 awards in six categories each year.

• #IndustrialMetaverse
• #KI
• #nvidia

Author

Susanne Weller

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Ascon Systems is being recognized for its Industrial Metaverse Portal, which launched in late 2023. The portal has already been deployed at the BMW Innovation Hub in Dingolfing. The hyperconvergent technology and digital twins the platform is built on are capable of controlling real production via the NVIDIA Omniverse and making flexible use of the information generated in earlier phases. The Industrial Metaverse Portal consolidates the capabilities of visualization and interaction with data in an intuitive and immersive way. This grants users full visibility of production processes so they can better understand and manage production.

Ascon Systems participated in the NVIDIA Inception Program through February 2024, after which the company became an NVIDIA Solution Advisor Partner. In March of 2024, Jens Mueller, CEO von Ascon Systems, held the keynote presentation “Bringing the NVIDIA Omniverse to Life” at NVIDIA GTC in California, one of the world’s largest AI conferences. In July he’ll be speaking at the Industrial Metaverse Conference in Ludwigsburg, Germany about best practices and new developments in the industrial metaverse.

For more information on the award, visit NVIDIA’s blog post.

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Mar 21, 2024

Hyperconvergence in manufacturing: increasing efficiency through IT innovation

From silos to synergies: industrial production is at a crossroads. To remain competitive in rapidly changing markets, companies must overcome supply chain disruption and make progress with their decarbonization and digitalization goals.

• #hyperconfergence
• #innovation
• #innovation
• #IT
• #techsimplified

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Susanne Weller

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From silos to synergies: industrial production is at a crossroads. To remain competitive in rapidly changing markets, companies must overcome supply chain disruption and make progress with their decarbonization and digitalization goals.

These challenges are overwhelming conventional IT infrastructures and the automation pyramid, which also encourages a silo mentality. Such systems block innovation and change, lead to inefficiency, prevent data integration, and make it more difficult to leverage synergies across the IT estate. Yet all these things are essential when running a modern manufacturing operation. That’s why manufacturers are increasingly implementing hyperconvergence (hyperconverged infrastructure or HCI) as they transform their facilities into smart factories. This solution allows them to break down silos, free themselves from the automation pyramid, and achieve an agile, data-driven production environment.

What exactly does hyperconvergence mean, and why does it hold the key to the future of manufacturing? Jens Mueller, CEO of Ascon Systems and one of the pioneers of integrating hyperconverged infrastructures, provides key insights and context.

What does hyperconvergence mean?

Jens Mueller: Hyperconvergence refers to IT infrastructure that combines hardware and software, servers, storage, and networking capabilities to form a scalable solution. The resulting platform is managed and controlled as a single system via software-defined technologies. And this is where we see the big difference in comparison to conventional systems with hardware and software in silos that can only be configured by means of time-consuming reprogramming. Hyperconvergence opens up these systems and results in greater efficiency, flexibility, and scalability of the IT infrastructure while simplifying management and accelerating service delivery. That’s why hyperconvergence is seen as laying the groundwork for Industry 4.0, which requires that systems be able to talk to one another.

How has hyperconvergence developed?

The term hyperconvergence comes from the world of IT. If we look back on the evolution of IT infrastructure, they were characterized by a clear distinction between servers, storage, and network components. Hardware and software were acquired separately and from different vendors. You had to configure and manage them separately, too. That led to significant complexity, inefficiency, and high costs. Convergent infrastructures emerged as a response to these limitations. Here, hardware components are preconfigured and pre-integrated. Once combined with the appropriate software, such infrastructures are designed to reduce complexity, enhance efficiency, and effectively manage the substantial data demands of our interconnected world. Hyperconvergence has evolved from this but is a completely new approach and the next stage of development. Here, everything is software-defined. Hyperconvergent systems consolidate physical hardware and use software to define and manage compute, storage, and network functions. This enables greater flexibility, scalability, and efficiency. Hyperconvergence thus represents the next big turning point in the IT world.

For which industries and companies is hyperconvergence important?

The focus of hyperconvergence is on increasing efficiency, reducing costs, and enhancing agility. These three factors are particularly important in manufacturing and automation, for instance for industrial equipment manufacturers, automakers, industrial suppliers, but also in pharmaceuticals and the healthcare sector in general. Hyperconvergence can accelerate productivity by providing flexible IT infrastructure you can scale rapidly. It offers better data management and promotes global collaboration because hyperconvergent infrastructures enable better teamwork across remote locations. We can see the positive impact for instance for our customer BMW. BMW has been testing our technologies at its Innovation Hub in Dingolfing to see how hyperconvergence in combination with digital twins affects efficiency. And of course with our technology partner NVIDIA as well.

What does this look like in practice?

Take the robot arm, for example: it has to move to do its work. It needs to be set up and assembled. That’s a physical process. That, of course, will not change. How you control the robot arm and how adaptable it will need to be in the future, that’s a completely different story than it was even 10 years ago. And in just a few years, there will be even more progress. Companies will have new possibilities for their production and be able to react with greater flexibility to the market. That, in turn, will give them the competitive advantage they need

How disruptive is hyperconvergence?

Hyperconvergence fundamentally changes the way companies design and manage their data centers and IT infrastructures. That makes it a strong disruptive force with respect to conventional technologies such as the automation pyramid. Although hyperconvergence has enormous disruptive potential for many industries, the degree of disruptiveness depends on the specific requirements and existing IT infrastructures of the respective company and the will to adapt and to innovate. In some cases, hyperconvergence can improve things as a gradual evolution. In other contexts, it can spur a revolutionary transformation of the organization’s IT strategy.

What conditions facilitate hyperconvergence?

Technological advances such as 5G, edge computing, and TSN (time sensitive networks) are driving to the development of hyperconvergence enormously. Especially with respect to performance and real-time applications. They expand the potential of hyperconvergence by enabling new use cases and establishing the preconditions for even tighter integration between IT and OT systems. This is particularly important for smart manufacturing and Industry 4.0 initiatives. New connectivity technologies play a key role in interconnecting devices and services. They help expand the limits of what is possible with hyperconvergent infrastructures while facilitating acceptance and adoption across many industries.

What impact do hyperconverged IT infrastructures have in companies?

In the future, companies will run their production operations like a modern data center. Highly standardized with as little vendor lock-in as possible with highly centralized processes and the ability to virtualize everything across multiple levels. This will make production more reliable and, above all, more resilient.

Hyperconvergence makes it much easier for companies to combine with a blockchain, to implement standards and ensure transparency. This is also important for the traceability of products and value chains which are increasingly quickly becoming a legal requirement, but create challenges for companies that conventional IT structures are not able to handle.

Manufacturers can free themselves from the rigid infrastructures of the past with hyperconvergence, create the conditions for a rapid digital transformation and transfer their operations into a new era of agile production.

Find out more: Jens Mueller is a guest on the “Digital 4 Leaders” podcast and talks in session #72 about hyperconvergence, data and digital twins in production (in German only).

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Feb 15, 2024

The industrial metaverse: what to know about key technologies, correlations, and benefits

• Digitaler Zwilling
• digitaltwin
• Effizienz
• Effizienz
• hyperconvergence
• Hyperkonvergenz
• IIoT
• Industrial Internet of Things
• Industrial Metaverse
• Interoperabilität

Author

Susanne Weller

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The Industrial Metaverse holds significant potential for the German economy. It’s particularly relevant across manufacturing industries such as industrial equipment and automobiles, but also in the energy, healthcare, logistics, and construction sectors. In their 2023 survey of U.S. manufacturing executives, “Exploring the industrial metaverse,” Deloitte and the Manufacturing Leadership Council found that 92% of the respondents’ companies are experimenting with or implementing at least one use case related to the Industrial Metaverse.

The industrial metaverse combines cutting-edge technologies including virtual reality (VR), augmented reality (AR), artificial intelligence (AI), the internet of things (IoT), and digital twins to generate a realistic simulation of the physical world in a digital space. It saves companies time and money by allowing them to design, plan, test, and optimize production lines without physical prototypes. In the industrial metaverse, training, remote maintenance, product design and development, and enhanced collaboration across geographical boundaries all become possible.

Certain conditions and technological foundations must be present before we can speak of an industrial metaverse. Once companies and industries have established this infrastructure, they can operate in a fully integrated digital ecosystem.

We have compiled below the key technologies that define the industrial metaverse as well as the outcomes and benefits that the industrial metaverse facilitates.

The 7 key technologies of the industrial metaverse

1. Immersive technologies: Virtual reality (VR) and augmented reality (AR) are key to providing users with an immersive experience. They make it possible to simulate or supplement real physical environments and objects in the digital world.
   
2. Digital twins: As digital representations of physical objects or systems, digital twins can analyze, simulate, and optimize existing processes before changes are made to physical equipment. They can also function as prototypes before a physical plant or a real process is created. In both cases, this saves time, labor, and energy.
   
3. The internet of things (IoT): A machine or system is part of the internet of things if it can send and receive data to and from another network or other machines. Various technologies such as Wi-Fi, Bluetooth, Ethernet, LTE, and 5G provide connectivity. To enable data acquisition, such devices come equipped with sensors. These sensors record physical data from the environment. The data can include speed, torque, flow, vibration, energy consumption, emissions, and more. IoT devices may also have actuators that can, for instance, open or close valves. Each device has a unique name that allows other devices in the network to recognize and address it. The IoT for industrial applications is referred to as the industrial internet of things or IIoT.
   
4. Artificial intelligence (AI) and machine learning (ML): AI and ML can analyze large amounts of data, recognize patterns, and make predictions. They support the automation and optimization of processes in the industrial metaverse.
   
5. High-performance networking technologies: Fast and reliable data transmission is essential in the industrial metaverse. Technologies such as 5G or future network standards offer the necessary bandwidth and low latency.
   
6. Interoperability and standards: Interoperability and common standards are necessary for different systems and technologies to work together seamlessly in the industrial metaverse. Interoperability includes data formats, protocols, and interfaces. It refers to the ability of systems to work together effectively, to exchange and interpret data, and to act correctly based on this data, regardless of their design or manufacturer. This requires standards which specify common protocols and interfaces, which in turn are the prerequisites for data transmission.
   
7. Scalable cloud infrastructures: Cloud computing provides the necessary computing power and storage capacity to support the complex simulations and large quantities of data in the industrial metaverse.

7 advantages for companies

How does a company benefit from engaging with the industrial metaverse and creating the conditions for it?

1. Efficiency gains and cost reduction: Companies that use VR, AR, and digital twins can virtually simulate and optimize production processes, leading to significant efficiency gains and cost savings.
   
2. Increased productivity through improved collaboration: Immersive technologies and cloud infrastructures promote collaboration across geographical boundaries and increase productivity.
   
3. Hyperconvergence for optimized resource utilization and scalability: Hyperconvergence refers to an IT framework that integrates compute, storage, and network resources into a single, software-defined platform. In the industrial metaverse, hyperconvergence enables efficient and flexible resource management. This leads to easy scalability, optimized performance, and reduced operating costs.
   
4. Predictive maintenance instead of unplanned downtime: The interplay of the IoT, AI, and machine learning enables predictive maintenance. Predictive maintenance lets companies know a machine requires maintenance before it breaks down. This way, they can plan maintenance work in advance and reduce downtime.
   
5. Accelerated innovation and product development: The ability to experiment and test prototypes in a virtual environment speeds up the innovation process and product development.
   
6. Increased security and compliance: Robust cybersecurity measures and data protection practices in the industrial metaverse protect critical data and ensure compliance with applicable regulations.
   
7. Promote sustainability and decarbonization: You can analyze material and energy flows in the industrial metaverse. Most importantly, virtual simulations can replicate resource efficiency measures, waste avoidance, and circular economy scenarios before they are physically implemented.

Continue reading:

• The Path to the Industrial Metaverse
• Digital Twin: Real or often only a shadow
• The Factory of the Future in the Industrial Metaverse: Utopia and Opportunity?

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Jan 11, 2024

Testing future business models: Successful X-Forge research project completed.

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Susanne Weller

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Our technologies optimize industrial processes for our customers and pave the way to data-driven manufacturing. What many people don’t know is that we are also using our technologies to conduct research and develop new business models. Collaborating with other companies allows us to uncover new opportunities and explore the role our solutions can play to help increase efficiency in the factory of the future.

We’ve recently achieved some research results that are especially promising. Over the last two years, we’ve examined the role of data in the product life cycle as part of the now concluded Product Life Cycle Enrichment as a Service (PLCEaaS) project working in collaboration with WITTENSTEIN SE, TruPhysics, and Fraunhofer IPA. It’s one of the four showcase projects of X-Forge, an Industry 4.0 research program funded by the German federal state of Baden-Württemberg as part of its Invest BW initiative.

X-Forge: Four showcase projects to research XaaS models in mechanical and plant engineering

There are currently not enough successful XaaS implementations in real-world mechanical and plant engineering contexts, especially among small and medium-sized enterprises (SMEs). X-Forge was created to change that. Research conducted as part of X-Forge aims to explore the development and potential commercialization of XaaS services for engineering applications and address lingering reservations. X-Forge comprises four showcase projects which examine different business cases for XaaS services. In each of the four projects, the participating companies developed processes in a way that made it possible to break them down into separate services which formed XaaS offerings. Fraunhofer IPA has described the process in detail on its website. Taken as a whole, the projects are intended to offer insights that can also be applied to other industries and enable fully XaaS-based models consisting of end-to-end production processes. The four showcase projects are Smart Factory as a Service, Productivity as a Service, Wood Working as a Service, and Product Life Cycle Enrichment as a Service (PLCEaaS), the last of which we participated in together with other companies.

PLCEaaS: Driving value creation with data

Participants in the X-Forge subproject PLCEaaS formed a temporary research alliance including  WITTENSTEIN SE, a specialist for mechatronic drivetrain technology with a focus on gearboxes, TruPhysics GmbH, a software development firm that makes simulation software for industrial and service robots, Fraunhofer IPA, and us, Ascon Systems, a technology company specializing in digital twins.

Using processes and data from WITTENSTEIN, we explored how expanding the digitalization of products and manufacturing processes can help ensure success in markets that are increasingly characterized by global competition. We analyzed the contribution of data and technologies to the product life cycle as well as possibilities for adding digital services to a company’s portfolio to enhance its positioning and achieve greater differentiation.

The product life cycle of a gearbox

The research project used data from WITTENSTEIN SE derived from the real-world product life cycle of a gearbox. This operating data came from product development, manufacturing, and quality control at WITTENSTEIN SE as well as data generated during installation of the product by an industrial machine manufacturer and the subsequent production data from an operator of the machine. We first acquired and analyzed the data. Then we modeled it on the demonstrator using both digital twins and standard connectors (in the OPC UA project) to acquire signals from all the sensors. Finally, we provided the data as microservices. This enabled acquisition of all sensor signals within their respective contexts stored inside a digital repository of the lifecycle, which is then available for use by an analytics toolkit for efficient evaluation.

Testing the future

The results of the PCLEaaS initiative offer analytical insights into data quality, data quantity, and the status of contextual information. These insights are crucial for analyzing causal relationships across the entire product life cycle. The PLCEaaS project ran for two years, making it possible to implement various approaches to data analysis and evaluation for maximizing efficiency gains in production. All the participants of X-Forge stand to profit from the results. Businesses in other industries can also use the approach to model, validate, and optimize business models and determine their feasibility for future use. The X-Forge project is ongoing. Additional information is available at the
Allianz Industrie 4.0 Baden-Württemberg website, at X-Forge on Linkedin, and on the Fraunhofer Institute for Manufacturing Engineering and Automation IPA website.

*XaaS stands for Everything-as-a-Service. That includes IT services as well as products such as software, hardware, and infrastructure. Basically anything a vendor chooses to rent out or offer as a subscription instead of selling it for a one-time fee. This model gives customers more flexibility and allows them to scale as needed.

Autorin
Susanne Weller

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Dec 14, 2023

How BMW is testing next-gen factory control with technology from Ascon Systems

Case Study

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Susanne Weller

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The manufacturing industry is undergoing a significant transformation. To stay competitive, manufacturers must explore new ways to become more agile, flexible, and efficient overall. Traditional automation technologies fall short of these goals, which means they are not by themselves sufficient to achieve transformation.
But what if there were a way to test future approaches to manufacturing without building entire factories? Is it possible to keep simulating various scenarios, approaches, and events until you discover real improvements in production processes — before investing in new facilities and technologies? The BMW Group’s answer is a decisive “yes.”
BMW built its Innovation Hub at Dingolfing, Germany for this very purpose. This real-world laboratory at the company’s largest European production site is where it tests future technologies and implements them as proofs of concept. From February to August 2023, Ascon Systems worked with NVIDIA and a team from the BMW Group on a groundbreaking two-phase project using the Innovation Hub. The goal was to find ways to control and monitor the factory of the future in real-time through a digital twin. Some of the questions explored include: How can we visualize equipment locations, movements, machine status, and process parameters in NVIDIA’s Omniverse? And how can we reconfigure and validate shopfloor processes and manufacturing cells?

Implementing the pilot phase with digital twins

Technicians built a 5-meter long functional scale model of an assembly line with workpiece carriers for the pilot at the BMW Innovation Hub. The automated model coordinates multiple assembly and inspection stations to recreate key capabilities in vehicle manufacturing.
The Ascon Systems Automation Platform and digital twins serve to model the process logic of the assembly line. The digital twins perform several functions here. They operate as IT services which control the equipment while also providing the company with comprehensive information on processes and completed products. The model assembly line illustrates how a real shop floor and digital twin can interact to meet the needs of day-to-day operations. It’s a compelling demonstration of how manufacturers can already use data to manage the factory of the future today in much the same way you would operate a data center.

Integration into NVIDIA’s Industrial Metaverse

Next, Ascon Systems used the Ascon Systems Metaverse Portal to connect the model assembly line to the NVIDIA Omniverse, the company’s industrial metaverse product.

The result is a synchronized link between the physical assembly line and the Omniverse.
The resulting virtual environment allows you to track, optimize, and make changes to live production, material flows, and equipment from anywhere in the world. You can also run replays for X-in-the-loop simulations. This industrial metaverse implementation is the only one of its kind in the world and offers the scalability and flexibility to fulfill countless use cases.

Solutions from the Industrial Metaverse: Three Examples

By integrating Ascon Systems technologies with the NVIDIA Omniverse, various issues in the design of production systems and facilities can be identified early and avoided. Here are three examples of potential applications:

• Collaboration: All employees have access to the same information in a clear and transparent manner. Whether it’s the facility planner checking the current status in Plant X, the process manager monitoring the condition of their equipment, or an employee seeking information about the last maintenance, these applications enable staff to observe the actual value stream in their factory. They receive information in the context of the relevant processes and the real-world activities taking place.

• Anywhere: Whether on-site or remotely, you can access real manufacturing data from anywhere in the world. Employees no longer need to be physically present to obtain information, monitor processes, or test changes. This translates to increased efficiency on all levels, including significant cost savings from reduced travel expenses.

• Intuitive Operation: An appealing and modern graphical interface displays information on tablets or computers. Operators can access process parameters, quality data, and faults with excellent usability including mouse-over tips and intuitive navigation.

Disruptive Potential in Industrial Automation

The results of the testing demonstrated just how significant an improvement in the planning and operation of production systems and factories is possible with a seamless combination of collaboration and simulation technologies. Neither the visualizations on the automation platform nor the changes in the digital twin and, consequently, the real shop floors, required programmers or automation experts. The results also illustrate how a new technological approach to daily tasks in planning and control can lead to a resilient factory, where processes are implemented in a resource-efficient, globally connected, and highly flexible manner.

Conclusion

The test was a resounding success. The implementation in the industrial metaverse is the first of its kind anywhere in the world. It is scalable, flexible, and an example of how production in the industrial metaverse leads to resilience and efficiency.

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