Digital Twin Flashcards
Digital Model
physical object -> digital object (NO AUTOMATIC DATA FLOW BETWEEN)
manual data flow in both directions
Digital Shadow
automatic data flow from physical object to digital object, manual data flow from digital object to physical object
Digital Twin
automatic data flow between physical object and digital object
Current trends
Big trend in customized products
Modular product architecture
Parallel product series
The life-time of a product VS. a production system: miss match, products life time is much less.
Changing demands on production systems
Modularity: Modular equipment configurations
Convertibility: Design for changeable functionality
Scalability: Design for changes in capacity
Designability: Designed for real-time diagnostics.
Integrability: Flexible interfaces for fast integration
Benefits w. DT
Shorter lead-time
Reduced costs
Test and development in a secure environment
More effective change and improvement work
Easier maintenance and troubleshooting
DT - Complexity and scale
There is a trade-off between business value and the cost and complexity of the digital twin, you should investigate if the part is critical enough to create a digital twin – coffee cup or electrical motor.
Examples of different abstraction levels
Production line: not so common, not so detailed (abstraction level low), can’t include all sensors.
Production cell
Machine (Individual equipment)
Manufacturing process
Shop-floor operator: analyse ergonomics, train operators.
Work environment
Technology Implementation Framework
- Identify: Curate business problems and use cases.
- Build: Develop a centre of excellence for the technology.
- Sponsor: Convince an Executive sponsor of the Value. Identify the stakeholders in the DT process.
- Align: Identify integration partners and technology providers.
- Partner: Commit to partners.
- Execute: Implement Technology Solution.
A digital twin can change the production environment and the way of work
DT Life-cycle perspective
Engineering -> Commissioning/implementing -> Operation & service -> Modernization
More implementation challenges
Scaling technology outside the proof of concept – you build a digital twin prototype that does not capture the evolvement of the process you want to mimic
Brownfield implementation - harder to implement digital technologies to old machines and factories.
Sourcing the right talent – Constructing a digital twin requires interdisciplinary teams with different background and talent (sometimes it’s also necessary to upskill their already existing talent)
Infrastructure and social change – We already understand the technology to a degree, however we need to handle the social changes
Sources of risks with Digital twins
Infrastructure cost
Granularity of digital twins coverage
Cross-functional alignment
Leadership & change management
Cybersecurity
The digital Twin as a decision support tool , how to use?
Building a digital model to digitally duplicate or “twin” a physical equipment or system is one important step to create a digital twin. List and discuss the requirements on such a model, when it is to be used for creating a digital twin?
- Accurate Representation:
**Geometry: **The digital model must replicate the exact geometric shape, dimensions, and structure of the physical equipment or system.
**Materials: **Accurate representation of the materials used in the physical counterpart is crucial for simulating behaviors accurately.
Sensors: Integration of sensors and IoT devices to collect real-time data, providing constant updates about the physical object’s state. - Real-time Data Integration:
Sensor Data: Continuous data streams from sensors embedded in the physical system. This data provides insights into real-time conditions, performance, and usage patterns.
**IoT Connectivity: **Seamless integration with the Internet of Things (IoT) infrastructure to collect data from various sensors and devices. - Simulation Capabilities:
Physics-based Modeling: The digital model should incorporate physics-based algorithms to simulate behaviors accurately. This includes aspects like fluid dynamics, thermodynamics, and structural mechanics, depending on the nature of the physical system.
Predictive Analytics: Ability to predict future states based on historical data and trends, enabling proactive maintenance and decision-making. - Interoperability:
**Data Compatibility: **The digital model should be able to interface and exchange data with other systems, databases, and software tools within the organization.
**Standardization: **Adherence to industry standards to ensure compatibility and interoperability with other digital twin systems and tools. - Security:
Data Security: Robust security measures to protect the integrity and confidentiality of the data being collected and exchanged.
Access Control: Implementation of access control mechanisms to restrict data access to authorized personnel only. - User Interface and Visualization:
User-Friendly Interface: Intuitive interfaces and visualization tools for users to interact with the digital twin, monitor real-time data, and analyze simulations.
Augmented Reality (AR) or Virtual Reality (VR) Integration: Utilizing AR or VR technologies for immersive experiences, allowing users to visualize the digital twin in 3D space. - Lifecycle Management:
Version Control: Mechanisms to track versions and changes made to the digital twin over time, ensuring that users are working with the most updated version.
Historical Data Storage: Storage and management of historical data for analysis, compliance, and auditing purposes. - Scalability and Performance:
Scalability: The digital twin model should be scalable to handle increasing amounts of data and complexity as the physical system evolves or expands.
Performance Optimization: Algorithms and technologies to optimize the performance of simulations and real-time monitoring, ensuring timely and accurate results. - AI and Machine Learning Integration:
Pattern Recognition: Implementing machine learning algorithms for pattern recognition within the data, enabling the digital twin to identify trends, anomalies, and potential issues.
Cognitive Abilities: AI-driven cognitive capabilities for advanced decision-making, enabling the digital twin to recommend actions based on the analyzed data.
