AWS IoT TwinMaker Documentation

AWS IoT TwinMaker is designed to make it faster and easier for you to create and use digital twins to optimize industrial operations, increase production output, and improve equipment performance. Digital twins are virtual representations of physical systems that are updated with real-world data to mimic the structure, state, and behavior of the systems they represent. With AWS IoT TwinMaker, you can use built-in connectors or create your own connectors to access and use data from a variety of data sources, such as equipment sensors, video feeds, and business applications. Import your existing 3D visual models to create digital twins of your facilities, processes, and equipment that update with data from connected sensors and cameras, visualize insights and predictions based on the data, and raise alarms to identify when data or predictions deviate from expectations. Integrate these digital twins into web-based applications that allow your plant operators and maintenance engineers to monitor and improve your operations.

AWS IoT TwinMaker provides several built-in data connectors for other AWS services that are designed to allow you to collect, organize, and store equipment and time-series sensor data, and capture, process, and store video data. AWS IoT TwinMaker also provides a framework for you to create custom data connectors to use with other AWS or third-party data sources. These data connectors allow your applications to use the AWS IoT TwinMaker unified data access API to read from and write to the different data stores without needing to query each data source using their own individual API.

To model your physical environment, you can create entities in AWS IoT TwinMaker that are virtual representations of your physical systems, such as a furnace or an assembly line. You can also specify custom relationships between these entities to represent the real-world deployment of these systems. You can then connect these entities to your various data stores to form a digital twin graph, which is a knowledge graph that structures and organizes information about the digital twin. As you build out this model of your physical environment, AWS IoT TwinMaker can create and update the digital twin graph by organizing the relationship information in a graph database.

With AWS IoT TwinMaker, you can build a 3D digital twin by using your existing and previously built 3D visual models, such as CAD files, Building Information Modeling (BIM) files, or point cloud scans. Using the AWS IoT TwinMaker scene composer and simple 3D tools, you can import these visual assets into a scene and position them to reflect your physical environment—for example a factory and its equipment. You can then add interactive video and sensor data overlays from the connected data sources, insights from connected machine learning (ML) and simulation services, and maintenance records and operational documents to provide you with a updated, spatially aware visualization of your operations. 

Once you’ve created the digital twin, AWS IoT TwinMaker is designed to provide a low-code experience for building a web application so your plant operators and maintenance engineers can access and interact with the digital twin. 

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