Across industries, we’re seeing more use of digital twins in manufacturing. Digital twin visualization technology pairs well with the sensors that manufacturers use to gather vital information on production processes. Now, the information being collected via smart manufacturing systems can be incorporated into visual, interactive models. These models help teams drive innovation.


What is Digital Twin in Manufacturing?

A digital twin in manufacturing is a virtual copy of a real-world component in the manufacturing process. Imagine the digital twin as an enhanced computer model, this digital representation uses inputs from a real-world component. The digital twin mirrors the real component’s status, functionality, and/or interaction with other devices.


What is AWS offering in DIgital Twin space?

AWS IoT TwinMaker is an AWS IoT service that you can use to build operational digital twins of physical and digital systems. IoT TwinMaker creates digital visualizations using measurements and analysis from a variety of real-world sensors, cameras, and enterprise applications to help you keep track of your physical factory, building, or industrial plant. You can use this real-world data to monitor operations, diagnose and correct errors, and optimize operations.


How it works?

AWS IoT TwinMaker makes it easier for developers to create digital twins of real-world systems such as buildings, factories, industrial equipment, and production lines. AWS IoT TwinMaker provides the tools you need to build digital twins to help you optimize building operations, increase production output, and improve equipment performance. With the ability to use existing data from multiple sources, create virtual representations of any physical environment, and combine existing 3D models with real-world data, you can now harness digital twins to create a holistic view of your operations faster and with less effort.


How to configure a digital twin in AWS

To fulfill the minimum requirements for creating a digital twin, you must do the following.

  • Model devices, equipment, spaces, and processes in a physical location.

  • Connect these models to data sources that store important contextual information, such as sensor data camera feeds.

  • The data sources include AWS IoT Sitewise, AWS Kinesis, AWS Time Stream  etc. 

  • Services like Ignition by Inductive Automation can be used to connect AWS data sources with the IoT devices.

  • Then we need to connect those data sources to AWS IoT TwinMaker using Entities in TwinMaker Workspace.

Adding Entities in TwinMaker

Adding Entities in TwinMaker

  • Once the entities are made. Then the scenes in Twinmaker need to be created.

  • The Scene needs to be connected to the entity using a tag.

Scene Builder In TwinMaker

Scene Builder In TwinMaker

  • Create visualizations that help users understand the data and insights in order to make business decisions more efficiently in this scene builder.

  • IoT TwinMaker provides a plug-in for Grafana and Amazon Managed Grafana.

  • Using AWS Grafana the digital twin can be published as a dashboard and we can build other insights around the digital twin using the data from it.

  • Make digital twins available to end users to drive business outcomes.

Why is AWS digital Twin not a standard in industry

  • AWS IoT TwinMaker is fairly new to the competition of digital twins.

  •  And it doesn’t have a stable release yet. So changes may be made to this service which might impact the performance of digital twins. 

  • The digital twin service provided by AWS is limited due to its lack of maturity. It might improve in future revisions.

  • It is not intuitive and very difficult in visualization. For example take a look at below visualization of plant floor on AWS Digital Twin which is even more difficult to navigate using mouse

Grafana dashboard of IoT Twinmaker

Grafana dashboard of IoT Twinmaker  

Hit or Miss

  • AWS IoT TwinMaker shows its newbie nature when it comes to features and usability.

  • It lacks a good interface to build scenes.

  • One cannot use twinmaker independently to get most use of it, it either needs Grafana or AWS managed grafana to create dashboards for Digital twin.

  • Even the initiative by AWS is promising it is not a good alternative to the digital twins available in the market. 

  • Right now we can say the digital twin service from AWS is a miss. But the future is promising for AWS ‘attempt at a digital twin.







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