Edge-AI Framework Real-Time PCB Fault is a M.Tech project topic for Electronics & Communication Engineering. It gives students a clear starting point for research, implementation planning, and documentation.
Edge-AI Framework Real-Time PCB Fault Project Details
| Abstract |
This project tackles the growing problem of e‑waste by using an edge‑based AI system to make printed circuit boards (PCBs) last longer. It does this with automated, real‑time fault detection. The method combines visual inspection and electrical diagnostics on a low‑power hardware platform. A small convolutional neural network (CNN) is trimmed with TensorFlow Lite so it can run quickly on a Raspberry Pi. The CNN looks at live PCB images to spot component defects and structural problems. At the same time, an analog‑to‑digital converter (ADC) turns analog signals from several inspection nodes into digital data, allowing continuous health monitoring and precise fault location. To keep the system up‑to‑date, it syncs
with the cloud using Rclone and Google Drive, and test sequences are stored in a GitHub repo so they can be used on many circuit layouts. Model training can be done remotely in Google Colab, and the system can be expanded with modular upgrades. This hybrid approach mixes computer vision with multi‑node electrical sensing, giving a solid way to support sustainable electronics manufacturing and repair. Fried Engineers helps with implementation, architecture advice, and model evaluation so students can analyze and simulate this multi‑modal edge diagnostic system.
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| Reference Paper |
Edge-AI Framework for Real-Time PCB Fault Detection with Cloud-Integrated Automation for Sustainable Electronics |
| Domain |
Embedded Systems / Edge AI |
| Sub-Domain |
VLSI & Embedded Systems / Embedded Systems / Edge AI |
| PDF Download |
View Source |
| Get Help |
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