- Admin
- August 1, 2025
Case Study
Enhancing PCB Quality Control with AI-powered Defect Detection
Client
A global leader in microinverter-based solar systems, home batteries, and energy management solutions sought to elevate the quality assurance of their PCB assembly lines. To minimize manufacturing defects in their microinverter boards and enhance production efficiency, they partnered with Ignitarium and AWS to implement an AI-driven real-time PCB defect detection solution.
Scope
Detection – Identify PCB defects like missing components, polarity issues, etc.
Inference – Perform real-time analysis on Jetson Orin for defect classification.
Annotation – Tag and label images for model training in the cloud.
Training, Reporting, Defect logging, Dashboards – Train AI models using annotated data on the AWS cloud.
Deployment – Push trained models from AWS to the edge.
Challenges
In the fast-paced production of microinverters, even minor defects such as missing components, reversed polarity, or slight angle shifts can compromise product reliability and performance. The customer needed a robust, automated solution capable of detecting such faults in real-time during manufacturing. Manual inspection was proving inadequate, time-consuming, and inconsistent. There was a need to implement an intelligent defect detection system to drastically improve quality control and reduce human dependency.
Solution
Ignitarium developed a comprehensive AI-based defect detection platform tailored to the customer’s PCB assembly lines. The solution integrated advanced image processing and machine learning capabilities, executed through a two-tier system:
Jetson Orin-based Desktop Application
This edge application facilitated the registration of new components, captured high-resolution images via a 4-camera hardware setup, and performed real-time inference to detect PCB defects. It also allowed operators to tag errors, contributing to continuous model improvement.
Training and CNN model management on AWS
Hosted on the customer’s AWS infrastructure, this application enabled image annotation, AI model training, and seamless deployment of updated models to the Jetson-based system. This centralized cloud setup ensured scalable and efficient model management across production lines.
Outcome
The solution enabled automated detection of key defects—missing components, reversed polarity, component angle shift, and partial damage—on microinverter PCBs. It significantly reduced manual inspection time and improved accuracy in identifying defects. The modular design and continuous learning loop ensured that the system could adapt and improve over time, driving consistent production quality.
Business Impact
Improvement in Manufacturing Efficiency
Eliminates human dependency on defect detection with a drop of 30% in scrappage rate
Improved accuracy
85% to 98% accuracy for different types of defect detections
Scalable and Adaptive System
Cloud-based training enable continuous improvement and easy adaptation to new component types
