A continual learning framework for defect recognition in additive manufacturing using a progressive online ridge regression approach

Currently, Additive Manufacturing is revolutionizing the production of complex and customized components across various industries, offering significant advantages in material efficiency, design flexibility, and rapid prototyping. Concurrently, Machine Learning models have become increasingly crucial in Additive Manufacturing, improving decision-making, process efficiency, predictive accuracy, and defect recognition. However, the practical implementation of Machine Learning models in production environments presents significant challenges, such as managing incremental knowledge without causing catastrophic forgetting of old knowledge. The complexity of the additive process and the large number of parameters often require training the model on new data without forgetting previously acquired knowledge. Continual Learning is an emerging practice for incremental knowledge management in Artificial Intelligence. This paper presents a novel Continual Learning approach for class-incremental learning tasks, called Progressive Online Ridge Regression (PORR), based on an extended version of Ridge Regression that fine-tunes a pre-trained Convolutional Neural Network, MobileNetV3. The method is applied to an image analysis problem to recognize defects in Powder Bed Fusion of Polymers. An open-access dataset has been analyzed to validate the approach. The experimental results demonstrate that the proposed approach reduces catastrophic forgetting by optimizing computational resource allocation with respect to accuracy, training time, CPU utilization, and maximum RAM usage.