Digitizing & automating vehicle assembly inspection with voice-enabled AWS services in 80 characters

Automating Quality Inspection on the Manufacturing Floor with AI and IoT

In today’s fast-paced automotive industry, manual quality inspection processes can hinder production efficiency and result in increased warranty costs. To address these challenges, implementing an AI-powered digital solution deployed at the edge can revolutionize the way defects are detected and corrected in real-time during assembly. This blog presents a cutting-edge Internet of Things (IoT) solution that automates and digitizes the quality inspection process for assembly lines, leveraging Machine Learning (ML) models and AWS IoT Greengrass.

Solution Overview:

The proposed architecture includes ML model training, defect data capture, data enrichment, data transmission, processing, and visualization. The AI model is trained to convert audio input into text, enabling quality inspectors to record defects verbally, which are then mapped to specific defect types by the ML model running on the edge device.

Figure 1. Automated quality inspection architecture diagram

1. Machine Learning (ML) model training: Utilize the open-source model whisper-tiny to convert audio to text.
2. ML model edge deployment: Deploy the ML model on AWS IoT Greengrass to perform real-time inference.
3. Voice-based defect capture: Capture voice inputs and convert them to text using the ML model on the edge device.
4. Add defect context: Enhance defect data with contextual information before transmitting it to the cloud.
5. Defect data transmission: Transmit defect data to AWS IoT Core securely using MQTT.
6. Defect data processing: Process incoming messages using AWS IoT Rules Engine and store data in DynamoDB.
7. Visualize vehicle defects: Expose defect data as REST APIs for visualization in web portals or mobile apps.

Steps to Setup the Inspection Process Automation:

Step 1: Setup the AWS IoT Greengrass device

• Create an Amazon EC2 instance with Ubuntu OS and install the AWS SDK for Python.
• Create an AWS IoT Greengrass core device and set up the Greengrass software.

Step 2: Deploy ML Model to AWS IoT Greengrass device

• Clone the repository with the ML model and deploy it to the Greengrass device.

Step 3: Setup AWS Lambda service to transmit validation data to AWS Cloud

• Create a Lambda function to publish defect data to AWS IoT Core.
• Import the Lambda function as a Component in AWS IoT Greengrass and deploy it to the device.

Step 4: Validate with a sample audio

Test the solution by publishing sample defect data and observing the ML model’s inference on the Greengrass device.

Conclusion:

By deploying AI and IoT technologies on the manufacturing floor, automotive OEMs can streamline their quality inspection processes, reduce errors, and improve production efficiency. This solution not only enhances defect detection but also enables real-time data transmission and visualization for informed decision-making. The architecture outlined in this blog can be adapted for various industries seeking to automate and digitize their quality processes.

About the Authors:

Pramod Kumar P is a Solutions Architect with expertise in IoT solutions on AWS.

Raju Joshi is a Data Scientist specializing in Big Data and ML solutions on AWS.