Digital twins in additive manufacturing: a state-of-the-art review

Additive manufacturing (AM) has surfaced as a pivotal component in the evolving field of intelligent manufacturing, offering an array of benefits compared to conventional production techniques. Nevertheless, the industry grapples with issues relating to manufacturing instability and inconsistent repeatability, making it challenging to meet desired microstructure and performance standards. The optimization of processing variables within specific equipment and parameter sets often necessitates expensive trial-and-error experiments, given the diversity and intricacy of AM process parameters. To mitigate these challenges, the digital twin (DT) technical concept has been implemented to bolster AM by offering real-time projection and mirroring of physical attributes for both the fabricated products and the AM machinery, thereby facilitating real-time feedback control to alleviate AM-induced defects and achieve optimal performance of the manufactured parts. DT techniques streamline process monitoring, performance prediction, anomaly detection, process parameter optimization, and production cost forecasting, thereby enhancing the entire AM process. Within the framework of Industry 4.0, DTs in AM have attracted considerable attention and experienced significant progress. Auxiliary techniques such as the Internet of Things (IoT), big data analysis, cloud manufacturing, and machine learning (ML) have substantially driven the expansion of DTs in AM. This review's contribution lies in the comprehensive analysis of how the digital twin (DT) technical concept has been introduced to enhance AM. This review examines existing literature on DTs in AM from six perspectives: background information, structural components, applications, directions for improvement, principal issues encountered, and potential research directions. It identifies current advancements, discusses applications across different domains, suggests areas for improvement, and outlines potential research directions. This review also identifies current advancements, discusses applications across different domains, suggests areas for improvement, and outlines potential research directions. These insights significantly contribute to the understanding and further development of DTs in AM within the context of Industry 4.0, offering a fresh perspective that aligns with the evolution of the intelligent manufacturing industry.