Mitigating domain shift in online process monitoring for material extrusion additive manufacturing via transfer learning

Abstract

The additive manufacturing (AM) method has experienced rapid growth in recent decades. However, its application in end-use products is constrained by printing defects. Therefore, online process evaluation and optimization are crucial for producing parts that meet quality standards. Data-driven methods have been extensively employed for in-situ process evaluation and control in various AM processes to establish correlations among process parameters, process information, and part quality. However, a critical weakness of these methods is their poor generalization, primarily due to two factors: the diverse nature of printing itself and the significant influence of environmental factors. This study proposes a framework for in-situ process evaluation and control of AM using transfer learning to address the domain shift problems. It highlights a significant domain shift issue in AM monitoring, especially when using deep learning models. A comprehensive monitoring system for material extrusion additive manufacturing is constructed to collect data from different domains, emphasizing the notable differences in data characteristics across these domains. Lastly, transfer learning methods are tested to tackle the domain shift issue in AM, enhancing the robustness and reliability of the monitoring system.