SINDIT: Simplifying smart manufacturing with digital twins

The publication titled “SINDIT: A Framework for Knowledge Graph-Based Digital Twins in Smart Manufacturing” introduces a framework for modern manufacturing. The paper focuses on the innovative use of digital twins to enhance operational efficiency in smart manufacturing environments. Its authors are An Ngoc Lam, Gøran Brekke Svaland, Miguel Ángel Barcelona, Shane Keaveney, Wissam Mallouli, Luong Nguyen, Assia Belbachir, Xiang Ma, Akhilesh Kumar Srivastava and Ahmed Nabil Belbachir, all of who work together in the COGNIMAN project.  This publication was presented at the 7th IFIPIoT 2024 International Workshop.

What is SINDIT?

SINDIT, short for SINTEF Digital Twin, with SINTEF being a partner in the project, is an open-source framework designed to help manufacturers build and use digital twins more effectively.

SINDIT aims to simplify the process of building digital twins, which are virtual representations of physical systems. These twins allow businesses to monitor, simulate and optimise processes in real-time, leading to smarter decision-making and greater operational efficiency.

Unlike existing tools that are often tailored to specific use cases or require proprietary systems, SINDIT focuses on flexibility, modularity and ease of use. Its core innovation is the integration of knowledge graphs.

A knowledge graph is like a map of information that connects different pieces of data and shows how they relate to each other. It helps systems understand and use this data more effectively, making it easier for the users to find answers and make decisions.

How SINDIT works

SINDIT’s framework is structured into four key layers, each with a specific role:

1. Data layer: This collects and organises information from various sources, including sensors, machinery, and databases. By supporting multiple communication protocols, it ensures compatibility with diverse industrial setups.
2. Digital twin representation layer: Using knowledge graphs, this layer creates a detailed and interconnected representation of the physical system. It facilitates analysing data and retrieving insights for real-time monitoring and decision-making.
3. Service layer: This provides tools for advanced analytics, simulations, and anomaly detection. By processing the data from the twin representation, it enables predictive maintenance and operational optimisation.
4. User interface layer: The framework’s dashboard and user-friendly tools make it simple for operators to visualise data, configure simulations and interact with the system.

Real-world applications

SINDIT is being tested in two of the COGNIMAN use cases.

1. Precision machining at GOIMEK: SINDIT supports robots used for deburring large metal parts. By integrating sensors and cognitive tools, it enables robots to work safely alongside humans, reducing manual labour while ensuring high-quality results.
2. Additive manufacturing at CROOM: In 3D printing, SINDIT helps monitor and analyse complex data from manufacturing processes, such as laser emissions, to detect potential defects early. This improves quality control and reduces costs.

Why it matters

The SINDIT framework addresses key challenges in the manufacturing industry, such as the need for flexible systems that can integrate diverse data sources and adapt to new technologies. By providing a modular and open-source solution, it lowers the barrier for manufacturers to adopt digital twins, enabling small and medium-sized enterprises to benefit from cutting-edge innovations.

Next steps

While SINDIT has already been used in previous EU projects like COGNITWIN, there are plans to further enhance its capabilities. Future developments include:

• Improved security features to protect sensitive data
• Expanded tools for machine learning and simulations to improve predictive capabilities
• Greater interoperability with industry standards to ensure seamless integration with other systems

Impact of SINDIT

SINDIT represents a realistic and impactful step forward for smart manufacturing. By combining knowledge graphs, modular architecture and user-friendly tools, it offers a practical pathway for businesses to embrace digital twins.

As manufacturing continues to evolve, frameworks like SINDIT will play a crucial role in helping companies remain competitive and efficient in a rapidly changing world.