Artificial Intelligence (AI) can significantly support manufacturing companies in their pursuit of operational excellence, by maintaining efficiency while minimizing defects. However, the complexity of AI solutions often creates a barrier to their practical application. Transparency and user-friendliness should be prioritized to ensure that the insights generated by AI can be effectively applied in real-time decision-making. To bridge this gap and foster a collaborative environment where AI and human expertise collectively drive operational excellence, this paper suggests an AI approach that targets identifying defects in production while providing understandable insights. A semi-supervised convolutional neural network (CNNs) with attention mechanisms and Layer-wise Relevance Propagation (LRP) for explainable active learning is discussed. Predictions but also feedback from human experts are used to dynamically adjust the learning focus, ensuring a continuous improvement cycle in defect detection capabilities. The proposed approach has been tested in a use case related to the manufacturing of batteries. Preliminary results demonstrate substantial improvements in prediction accuracy and operational efficiency, offering a scalable solution for industrial applications aiming at zero defects.