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Framework for the Development of a Process Digital Twin in Shipbuilding: A Case Study in a Robotized Minor Pre-Assembly Workstation
This article proposes a framework for the development of process digital twins (DTs) in the shipbuilding sector, based on the ISO 23247 standard and structured around the achievement of three levels of digital maturity. The framework is demonstrated through a real pilot cell developed at the Innovation and Robotics Center of NAVANTIA—Ferrol shipyard, incorporating various cutting-edge technologies such as robotics, artificial intelligence, automated welding, computer vision, visual inspection, and autonomous vehicles for the manufacturing of minor pre-assembly components. Additionally, the study highlights the crucial role of discrete event simulation (DES) in adapting traditional methodologies to meet the requirements of Process digital twins. By addressing these challenges, the research contributes to bridging the gap in the current state of the art regarding the development and implementation of Process digital twins in the naval sector.
Quantifiable Impact for Your Enterprise
Integrating Process Digital Twins in shipbuilding, as demonstrated, promises significant gains in operational efficiency and problem-solving, directly translating into business value.
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Process Anomaly Reduction
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
The ISO 23247 Standardized Digital Twin Framework
The proposed framework for developing process digital twins in shipbuilding adheres to the ISO 23247 standard, leveraging IoT principles. This standard organizes the digital twin into four primary layers: Observable Manufacturing Elements (OMEs), Data Collection and Device Control Entity (DCDCE), Digital Twin Entity (DTE), and Cross System Entity (CSE).
This structure ensures a robust and scalable integration of physical assets with their virtual counterparts, enabling real-time data flow, analysis, and control necessary for advanced manufacturing processes.
Progressive Digital Twin Maturity Levels
The research defines five stages of digital twin development, leading to three distinct levels of digital maturity based on functionality and autonomy:
- Stage 1: 3D Modeling - Basic virtual representation.
- Stage 2: Simulation Model - Incorporates behavior logic for experimentation.
- Stage 3: Connected DT (Level 1) - Real-time data feed from the plant for open-loop monitoring and analysis.
- Stage 4: Smart DT (Level 2) - Integrates AI for optimization, forecasting, and decision support (still open-loop).
- Stage 5: Autonomous DT (Level 3) - Achieves bidirectional control, enabling real-time autonomous decision-making and actuation on the physical system.
This phased approach allows enterprises to incrementally build DT capabilities aligned with their operational objectives and security constraints.
Process DT in a Robotized Shipbuilding Workstation
A real pilot cell at NAVANTIA—Ferrol shipyard serves as the case study, focusing on minor pre-assembly components. This cell integrates cutting-edge technologies like robotics, AI-automated welding, computer vision, visual inspection, and autonomous guided vehicles (AGVs).
The DT in this context enables precise monitoring and analysis of operations such as plate and profile identification, loading, robotized welding, visual inspection, and unloading. This application demonstrates the practical utility of process DTs in complex, highly customized manufacturing environments like shipbuilding, offering insights into optimizing intricate workflows.
Discrete Event Simulation (DES) for Digital Twins
The study highlights the crucial role of Discrete Event Simulation (DES) in adapting traditional modeling methodologies to meet the requirements of process digital twins. DES models are used to:
- Explore 'what-if' scenarios without disrupting real processes.
- Predict system behavior with confidence under specific conditions.
- Enable validation/diagnosis experiments by comparing simulation results with real plant data.
- Support forecasting/optimization experiments to predict future performance or optimize operations.
By feeding DES models with real-time or historical data, the DT provides a more reliable representation of actual plant operations, enabling sophisticated analyses and anomaly detection.
Core Concept: Our Digital Twin Definition
Accurate Virtual Representation "A digital twin is an accurate virtual representation of a system, whether it is a product or process, consisting of a set of digital information and 3D modeling which represents the behavior of that system in a trustworthy manner and is connected and integrated with its physical counterpart. Additionally, this integration allows for real-time monitoring and analysis of the system response to specific situations enabling the improvement and optimization of the system in terms of performance, operational efficiency, and responsiveness."Evolution of Digital Twin Maturity
3D Modeling
→
Simulation Model
→
Connected DT (Level 1)
→
Smart DT (Level 2)
→
Autonomous DT (Level 3)
| Layer | Role in Digital Twin | Shipbuilding Example (Navantia) |
|---|---|---|
| Observable Manufacturing Elements (OME) | Physical components being monitored or controlled. |
|
| Data Collection and Device Control Entity (DCDCE) | Acquires and preprocesses data from OMEs; translates control decisions. |
|
| Digital Twin Entity (DTE) | Manages the digital representation, ensures synchronization, provides functionalities. |
|
| Cross System Entity (CSE) | Provides the overarching security layer and ensures secure communication. |
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Navantia's Robotized Minor Pre-Assembly Cell: A Real-world DT
The case study demonstrates the practical application of the proposed framework within a robotized minor pre-assembly welding cell at NAVANTIA's Ferrol shipyard. This cell handles the assembly, welding, and visual inspection of ship components, integrating robotics, AI-automated welding, computer vision, and AGVs.
The Digital Twin, developed using Siemens Plant Simulation and Insights Hub, enables real-time monitoring of operations such as part identification, loading, welding, and inspection. By synchronizing the DES model with real-world data, the DT provides a baseline reference for continuous performance monitoring and anomaly detection, crucial for maintaining production efficiency in a complex, make-to-order environment.
Critical Capability: Real-Time Anomaly Detection
50% Welding Rate Anomaly Detected The DES model, calibrated with real-plant data, successfully detected a simulated 50% reduction in the workstation's welding rate, demonstrating the DT's ability to trigger alarms for process deviations and enable proactive intervention.Calculate Your Potential ROI
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Your Roadmap to a Smarter Future
Our proven implementation strategy ensures a seamless transition and maximum impact for your organization.
Phase 1: Discovery & Strategy
In-depth analysis of existing processes, infrastructure, and business objectives. Development of a tailored DT strategy and roadmap, identifying key integration points and desired maturity levels.
Phase 2: Data & Modeling Foundation
Establishment of data acquisition systems (DCDCE), physical component modeling (OME), and initial DES model development. Integration of core data platforms like Siemens Insights Hub (DTE).
Phase 3: Connected DT Development (Level 1)
Implementation of real-time data flow between physical assets and the DES model. Deployment of monitoring dashboards and anomaly detection mechanisms for operational visibility.
Phase 4: Smart DT Enhancement (Level 2)
Integration of AI and advanced analytics for predictive capabilities, optimization algorithms, and decision support tools. Refinement of models based on continuous learning from real-world data.
Phase 5: Autonomous Integration (Level 3 & Beyond)
Transition towards autonomous control, where the DT can execute decisions and control physical systems bidirectionally, while ensuring robust security and safety protocols (CSE).
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