Digital twins for additive manufacturing: A state‐of‐the‐art review

With the development of Industry 4.0, additive manufacturing will be widely used to produce customized components. However, it is rather time‐consuming and expensive to produce components with sound structure and good mechanical properties using additive manufacturing by a trial‐and‐error approach. To obtain optimal process conditions, numerous experiments are needed to optimize the process variables within given machines and processes. Digital twins (DT) are defined as a digital representation of a production system or service or just an active unique product characterized by certain properties or conditions. They are the potential solution to assist in overcoming many issues in additive manufacturing, in order to improve part quality and shorten the time to qualify products. The DT system could be very helpful to understand, analyze and improve the product, service system or production. However, the development of genuine DT is still impeded due to lots of factors, such as the lack of a thorough understanding of the DT concept, framework, and development methods. Moreover, the linkage between existing brownfield systems and their data are under development. This paper aims to summarize the current status and issues in DT for additive manufacturing, in order to provide more references for subsequent research on DT systems.