International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | PCB Error Detection using IoT on a Conveyor Belt |
|---|---|
| ABSTRACT | This paper presents a low-cost, IoT-enabled Automated Optical Inspection (AOI) system for detecting printed circuit board (PCB) manufacturing defects. To maximize system stability, the architecture divides the workload: an ESP32-S3 edge device handles real-time hardware actuation (ultrasonic proximity detection and conveyor motor control), while a local server handles intensive image processing. Overcoming the resolution limits and routing complexities of standard microcontroller cameras, the vision subsystem utilizes a commercial smartphone's high-resolution IP video stream. Upon detecting a PCB, the ESP32-S3 halts the conveyor and wirelessly triggers the server to capture an image and execute OpenCV-based image subtraction. This identifies defects like missing or misaligned components, demonstrating a highly scalable and budget-friendly machine vision solution for small enterprises. |
| AUTHOR | PROF. PRATIK TAWDE, MAYANK SHIRKE, SUMEDH YADAV, MIR SONAWANE, HASIB MEER Department of Electronic and Telecommunication Vidyalankar Polytechnic, Mumbai, Maharashtra, India. |
| VOLUME | 182 |
| DOI | DOI: 10.15680/IJIRCCE. 2026.1403052 |
| pdf/52_PCB Error Detection using IoT on a Conveyor Belt.pdf | |
| KEYWORDS | |
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