PCB Defect Classification with Data Augmentation-Based Ensemble Method for Sustainable Smart Manufacturing

In the rapidly evolving field of printed circuit board (PCB) manufacturing, automated optical inspection (AOI) systems play a critical role but often face challenges such as computational inefficiencies, high costs, and limited defect data. To address these issues, we propose an ensemble methodology that combines lightweight models with custom data augmentation techniques to enhance defect classification accuracy in real-time production environments. Our approach mitigates overfitting in small datasets by generating diverse models through advanced data augmentation and employing feature-specific validation strategies. These models are integrated into an ensemble framework, achieving complementary results that improve classification accuracy while reducing computational overhead. We validate the proposed method using two datasets: the general classification dataset CIFAR-10 and an on-site real-world PCB dataset. With our approach, the average accuracy on CIFAR-10 improved from 97.6% to 98.2%, and the accuracy on the PCB dataset increased from 81% to 89%. These results demonstrate the method’s effectiveness in addressing data scarcity and computational challenges in real-world manufacturing scenarios. By improving quality control and reducing waste, our method optimizes production processes and contributes to sustainability through cost savings and environmental benefits. The proposed methodology is versatile, scalable, and applicable to a range of defect classification tasks beyond PCB manufacturing, making it a robust solution for modern production systems.