A digital twin hierarchy for metal additive manufacturing

Digital twins present a conceptual framework for product life-cycle monitoring and control using a simulated replica of the physical system. Since their emergence, they have garnered particular attention as a shift away from costly physical testing and towards the use of high fidelity simulations, sensor data and intelligent control. Metal additive manufacturing (AM), a 3D printing technology prone to defects, requires a digital twin capable of tackling issues of printed part qualification, certification and optimisation. In this paper, we evaluate the key features specific to metal AM and review the current literature of modelling, sensing, control and machine intelligence. We find that the body of research toward the development of an metal additive manufacturing (AM) digital twin can be organised logically into a hierarchy of four levels of increasing complexity. The elements composing each level require deep integration and we highlight the key enabling technologies: surrogate modelling, in-situ sensing, hardware control systems and intelligent control policies. Our proposed digital twin hierarchy for AM provides a developer framework for engineering digital twins, both for AM and other intelligent manufacturing systems.(c) 2022 Published by Elsevier B.V.