Kolmogorov-Arnold Networks (KAN)-enabled Incremental Learning for Online Process Monitoring of Additive Manufacturing

This paper explores the advances of Kolmogorov-Arnold Networks (KANs) with potential applications in additive manufacturing (AM), such as the online process monitoring with competitive cost efficiency. With unique architecture and flexible activation functions, the emerging KANs provide a promising alternative to the multi-layer perception (MLP)-based models, enabling better modeling capability of complex AM processes, especially with the need to involve incremental learning. The proposed knowledge distillation (KD) empowered CNN-KAN framework integrates convolutional neural network (CNN) with KAN to model the complex AM processes while addressing challenges such as catastrophic forgetting in domain incremental learning. Utilizing the KD and flexible activation functions, the framework enables accurate anomaly detection and real-time process monitoring, even in dynamic and evolving data environments. The framework’s novelty lies in its first-time integration of KANs, CNN-based feature extraction, and KD-based incremental learning specifically tailored to dynamic AM processes. The experimental results highlight the effectiveness of the proposed method through a practical case study in AM, showcasing the promising future of the proposed KAN-enabled incremental learning framework.