Utilização de Inteligência Artificial e IoT para Inspeção e Detecção de Defeitos em Placas de Circuito Impresso

Abstract

The increasing complexity in the manufacturing of printed circuit boards (PCBs) has posed significant challenges for industries, particularly in the inspection and detection of defects. This study proposes the use of Artificial Intelligence (AI) and the Internet of Things (IoT) as tools to optimize fault detection in PCBs, focusing on reducing human errors and improving operational efficiency. The research is based on the implementation of an automated inspection system, utilizing neural networks for defect classification and IoT sensors for real-time monitoring of manufacturing conditions. The main goal of the dissertation is to develop an efficient analysis model to identify early faults, enhance product quality, and reduce operational costs. The study involved building a functional prototype that integrates AI and IoT, validated in a real production environment. The results showed that the proposed approach could detect defects with higher accuracy, leading to significant improvements in the efficiency of the PCB production line.