How Our AI Model Cut Manual Inspection Effort by 50% for a Leading Automaker
Overview of Our Automobile AI Model Case Study
A leading automobile manufacturer (under NDA) approached us to improve the quality control of key vehicle components like engine parts, brake assemblies, and dashboard panels. Manual inspections were time-consuming and prone to oversight, especially during high production runs.
Our AI agent development company has developed a computer vision–based AI model that scanned components in real time. This model can identify visual anomalies like cracks, scratches, or misalignments, and triggered alerts before the parts reached final assembly. Our solution has reduced the load on human inspectors, and helped prevent defective vehicles from being shipped out.
Automobile Manufacturing
AI Model Development
Our Process of Developing an AI Model for Real-Time Component Inspection
We designed a scalable AI agent for manufacturing that could detect surface and structural issues during the production cycle before the parts reached final assembly.
High-Resolution Image Collection
We installed industrial cameras at inspection stations and captured images of parts across multiple angles, batches, and lighting conditions.
Model Development Using CNNs
Our AI developers trained a deep learning model using Convolutional Neural Networks (CNNs) to detect issues like surface cracks, misalignments, dents, and finish inconsistencies.
Edge Deployment on Factory Floor
The trained model was deployed on edge devices. This enabled instant analysis of components as they moved along the conveyor. Latency was kept under one second per inspection.
Integration with Quality Dashboard
Detected issues were pushed to a real-time dashboard accessible to floor supervisors. They could take immediate action, mark defects, and trace error patterns across shifts.
