Published Paper Details:

This research presents a novel approach for real-time fault detection for PCB using AI and Python. The increasing complexity of electronic devices has led to a rising demand for efficient fault detection mechanisms in Printed Circuit Boards (PCBs). The integration of Internet of Things (IoT) technologies with advanced Artificial Intelligence (AI) algorithms presents a promising avenue for addressing this need. This paper proposes a novel approach leveraging Convolutional Neural Networks (CNNs) for the automated detection of faults in PCBs, facilitating real-time monitoring and diagnostics within IoT frameworks. The proposed system begins by acquiring high-resolution images of PCBs using specialized cameras or sensors, capturing intricate details of the board layout and components. These images serve as input data for the CNN model, which is trained on a comprehensive dataset comprising various types of faults, such as short circuits, open circuits, and component defects. Through extensive training, the CNN learns to discern patterns and anomalies, establishing a robust fault detection framework. Utilizing the inherent ability of CNNs to extract hierarchical features from images, the model accurately identifies and localizes faults within the PCBs. The integration of this AI-based solution within an IoT infrastructure enables seamless connectivity and data transmission, allowing for remote monitoring and analysis. Detected faults trigger immediate alerts or notifications, empowering timely interventions to mitigate potential issues, thereby enhancing the reliability and performance of electronic systems. The effectiveness of the proposed AI-driven fault detection system is validated through extensive experiments and evaluations, demonstrating high accuracy and reliability in detecting various types of faults in diverse PCB designs. The integration of CNN-based fault detection within IoT frameworks showcases its potential to revolutionize fault diagnosis in electronic systems, paving the way for more resilient and self-monitoring devices in the era of interconnected smart technologies