Azure IoT Workshop: Quality assurance for soda can manufacturing company Fabrikam

Introduction and Scenario

Fabrikam is a company that manufactures soda cans. As part of their quality assurance process, they need to make sure that the cans stay upright at all times as they move through different conveyor belts. Misplaced or fallen over cans can not only slow down their production lines but also decrease the overall quality of their products (ex. during the label printing phase). Fabrikam currently runs three production lines in parallel, each producing hundreds of cans per minute.

Fabrikam already has monitoring cameras in place that human operators use to perform real-time visual inspection of the production lines. They anticipate great efficiency and quality improvements if they could start automating the process.

Fabrikam's requirements in a nutshell

• Process video streams directly at the edge, in their facility. They need to be able to detect anomalies fast in order to take action, and can’t afford the latency a cloud-based computer vision service would provide ;
• Detect anomalies, ex. alert operators when cans have tipped over ;
• Process multiple & heterogeneous video streams, since they have several production lines, each using different models of monitoring cameras ;
• Manage their equipment remotely, ex. as their processes evolve and improve over time, they need to be able to deploy updates such as new computer vision models to detect new kinds of anomalies.

Outline

In this workshop, you will be building Fabrikam's real-time, AI-powered, quality assurance solution.

To satisfy the real-time, multiple cameras, custom AI model requirements, we'll build this solution using NVIDIA Deepstream on an NVIDIA Jetson Nano device.

We'll build our own AI model with Azure Custom Vision. We'll deploy and connect it to the Cloud with Azure IoT Edge and Azure IoT Central. Azure IoT Central will be used to do the monitoring and alerting.

1. Setting up an NVIDIA Jetson Nano and installing Azure IoT Edge
2. Setting up an IoT Central application for video analytics
3. Remotely operating the solution
4. Training and deploying a custom AI model for detecting visual anomalies
5. Sending alerts

Supporting slide deck

No supporting slide deck is available at the time of writing.

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