Digitalization The Digital Twin as a Pacemaker of Industrial Manufacturing

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Those who understand the lifecycle of an industrial machine as a data-driven cycle can use resources more efficiently, preserve expertise, and drive innovations in a well-founded manner. Openness, interface compatibility, and a focus on value creation turn digital twins into active decision-making partners.

The lifecycle of a machine begins already in the early project planning phase—and countless pieces of information are generated: CAD models, CAE data from FEM simulations, layout studies, and feasibility analyses. Additionally, there is structured information from project planning tools and ERP systems—such as budget constraints, supply chains, or resource availability. Investment and amortization calculations are also part of this phase.

Here lies a often untapped data treasure. To systematically unlock it, a consistent digital architecture is needed, along which information from different disciplines can be consolidated and flow through standardized interfaces. Platforms like Hexagon's Smart Digital Reality provide a framework in this phase to merge CAD, simulation, and project information into an initial Project Twin.

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Simulate Instead of Speculate

As a connected single source of truth, the digital twin links planning data, information from MES and ERP systems, current operating states, maintenance histories, and sensor data from ongoing production using open APIs. This creates a holistic, dynamically growing model that does more than merely reflect the status quo: it makes concrete risks, weaknesses, and improvement potentials transparent and serves as the basis for technical and economic decisions.

Simulations thus become an interactive planning tool. From thermal stress analyses and vibration simulations to resource planning, "what-if" scenarios can be explored in granular detail. For example: How does the cycle time change with the introduction of a collaborative robot? How do tool change cycles affect output? Such considerations can be quantified before commissioning using integrated simulation solutions, for instance with MSC Software or Simufact—delivering significant time and cost savings compared to traditional trial-and-error planning.

Clean Start, Clean Operation

With the transition to physical reality, the digital twin gains a new dimension: reality capture technologies such as high-resolution 3D laser scans or photogrammetry models document the actual installed system. The comparison with its virtual model reveals deviations—for example, due to adjustments during assembly, tolerance deviations, or subsequent modifications. This data flows back into the twin and completes it into a precise "as-built" model.

In addition, modern twins link real components with semantic depth: When was which component installed? What is its lifespan? Which material batch was used? Information of this kind can be automatically recorded and versioned through solutions like HxGN EAM (Enterprise Asset Management)—which proves beneficial later when it comes to maintenance, traceability, or deconstruction.

During ongoing operations, the digital twin becomes the central "hub" for process understanding. Real-time data from controllers, for example via OPC UA, management systems (MES), energy management, condition monitoring, or even air quality and climate sensors, flows into the model in real time. This data transforms the twin into a continuously updated representation of reality—a so-called live twin. Based on this, production managers gain reliable insights into performance: Does the system performance systematically deviate from the design value? Are there fluctuations in energy consumption? Which environmental conditions affect process performance?

Reliable Foundation Instead of Gut Feeling

Maintenance processes have long been heavily experience-driven. With the digital twin, data-driven methods are now being introduced. Especially for younger specialists who cannot draw on decades of their own experience, the digital twin provides an objective basis for decision-making. Systems like HxGN Predictive Maintenance analyze sensor patterns and machine logs, compare them with historical reference data, and suggest preventive measures—before costly failures occur.

Beyond mere maintenance and repair, continuously maintained digital twins provide the data foundation for investments in automation. Which processes can be implemented using robots? Where does retrofitting make economic sense? How can personnel resources be optimally combined with existing systems? Using simulation-based feasibility studies—for example in conjunction with HxGN Virtual Commissioning—scenarios such as the introduction of AMR (Autonomous Mobile Robots) or CNC retrofitting can be tested in advance on the digital shop floor. This provides companies with reliable decision-making foundations, such as for prioritizing CAPEX measures or assessing ROI potential.

The end of a machine's lifecycle is also part of the cycle. The digital twin documents all modifications, spare parts, material information, and maintenance histories—thus providing the foundation for an orderly dismantling or resource-efficient recycling in line with the ESG goals of the respective manufacturing company. Which components can be recycled, which can be safely dismantled, and which machines can be repurposed for other uses?

Digital Twin As A Change Companion in Demographic Transition

As experienced professionals retire, knowledge becomes a scarce resource. Younger industrial specialists see themselves as connected workers and expect digitized, agile workflows. In addition, comprehensive knowledge management is essential to preserve specialized expertise across generations.

In this context, the digital twin becomes an interactive knowledge keeper: 3D models of machines can be integrated into augmented or virtual reality environments. New employees can then virtually explore the system, test functions, or simulate maintenance processes—long before working on the actual machine. The benefit: reduced onboarding time, standardized training levels, and more reliable overall knowledge transfer. Especially in industries with high demand for skilled personnel— such as energy, chemistry, or pharmaceuticals—this creates a sustainable knowledge ecosystem.

With demographic change, the question of how to optimally utilize existing skilled workers is becoming increasingly urgent. Automated processes and AI-driven decisions are an important lever in this context. On a meta-level, for example, artificial intelligence could analyze existing processes within a facility and provide recommendations on where the use of robots would be most economically viable.

Digital twins embody the transition from isolated digitalization initiatives to a systemic understanding of industrial processes as lifecycles—data-driven, interconnected, and future-oriented. They realize their full potential when data silos are eliminated and a seamless data flow across planning, operation, and optimization is achieved. Thus, the technology is not an end in itself. What matters is its integration into everyday manufacturing. Technology partners like Hexagon, which combine CAD, metrology, simulation, and IoT expertise under one roof, provide the necessary combination of detailed insight and holistic perspective.

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