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This wiki walks you through the process of building a digital twin application using AWS IoT TwinMaker, and how to build your own digital factory.
After going though this wiki, you will learn how to build up your own 3D models which represent specific floors and rooms from a physical factory and equipment. You will use AWS IoT TwinMaker Console and the AWS IoT TwinMaker AppKit as tools to create your 3D scene and dashboards. The wiki will also show you the ways to customize your process panel, building on top of AWS IoT TwinMaker knowledge graph.
AWS IoT TwinMaker is a powerful tool that enables you to create and manage virtual twins for various applications. Whether you're a developer, researcher, or enthusiast, this article will serve as your starting point to effectively utilize AWS IoT TwinMaker's capabilities. Follow the how-to guide below to embark on your journey with AWS IoT TwinMaker.
When building your own digital twin solution, it is important to consider various factors such as system architecture, the utilization of AWS IoT TwinMaker service, dependency data sources, related AWS services, communication and data flow, visualization and user interfaces, scalability and resilience, security and compliance, as well as integration and extensibility. In this section, we will use the CookieFactory V2 Architecture as an example to demonstrate and showcase our Twin solution for reference purposes. By examining this architecture, you will gain insight into how these considerations are applied in practice.
To create an accurate and dynamic digital twin using AWS IoT TwinMaker, integrating the acquired data is essential. The first necessary step is to define data models within the AWS IoT TwinMaker solution. Once the models have been defined, the next step is to identify the data sources that provide the necessary information for building an accurate representation of the physical assets.
Common data sources for your AWS IoT TwinMaker solution may include sensors and IoT devices, external systems, or historical data. These sources play a crucial role in acquiring the relevant data needed for your digital twin.
Once the data sources have been identified, the next step is to establish a data ingestion process. This process involves collecting and ingesting the data into your AWS IoT TwinMaker solution. The data ingestion process ensures that the acquired data is properly received and incorporated into the digital twin.
In the following sections, we will provide detailed instructions and guidance on how to define data models, identify data sources, and establish the data ingestion process within your AWS IoT TwinMaker solution. These step-by-step instructions will help you achieve your goals and successfully integrate the acquired data into your digital twin.
You will leverage AWS IoT TwinMaker's capabilities to visualize and manage your digital twin in a 3D environment. To create an immersive 3D representation of your physical assets within the digital twin, you will need to import 3D models. AWS IoT TwinMaker supports various file formats such as GLTF, and GLB. You can place and position them within the virtual environment of AWS IoT TwinMaker, then visualize your digital twin in the 3D environment provided by AWS IoT. This allows you to gain a comprehensive view of your assets and their relationships, enabling better insights and analysis. This will give you the ability to gain a better understanding of your assets and make informed decisions based on the virtual representation of your physical infrastructure.
In the following sections, we will provide detailed instructions and guidance on how to import your 3D model into AWS IoT TwinMaker and how to bind AWS IoT TwinMaker resources on top of them.
The KnowledgeGraph Query phase empowers you to extract valuable information from your digital twin using TwinMaker's querying capabilities. By defining the KnowledgeGraph schema and constructing queries, you can retrieve specific data and gain insights into the relationships and properties of your assets within the digital twin.
This session focuses on providing intuitive and interactive interfaces to visualize and interact with your AWS IoT TwinMaker. In this section, we will provide detailed instructions and guidance on how to use the AWS IoT Application Kit to develop a user-friendly web application. This application will enable you to easily view your 3D models, dashboards, and alarms associated with your digital twin.
By following the instructions provided, you will be able to leverage the capabilities of the AWS IoT Application Kit to create a web application that enhances the visualization and user experience of your digital twin. The application will enable you to navigate through the 3D models, access relevant dashboards, and receive notifications about alarms or critical events associated with your assets.