Implementation of a digital twin for wire arc additive manufacture

In this paper, the authors describe the design, implementation and testing of a prototype digital twin of a manufacturing cell that can be used for wire arc additive manufacture. We have used Nvidia’s Omniverse as the core platform due to its open interface, which facilitates easy integration with open source and proprietary CAD, CAM and CAE tools. The digital twin breaks down the functional silos between design, manufacture, operations and maintenance, providing a full digital thread of the processes and a digital passport for the component. The implementation collects, stores and links all data associated with a manufactured part. Using the manufacture of a fusion power plant component as a case study, interaction with the digital twin starts with the engineer creating the original CAD model. Once complete, the CAD model is processed (by software) to prepare instructions for a robot arm to build the part. The manufacturing cell is equipped with various sensors and cameras. All the instrument data is streamed to the digital twin as the build progresses. To facilitate a loop back from manufacturing to physics-based modelling, a layer-by-layer build geometry is digitised using a laser scanner mounted to a second robot arm. The geometry is passed to a proprietary finite element package for meshing and residual stress analysis. All data captured by the twin is processed and documented, employing automation where possible, reducing the need for human involvement. After installation in the fusion power plant, the complete digital record can be retrieved at any time during the lifetime of the component. If the power plant is instrumented, the operating conditions can also be streamed to the digital twin, informing maintenance schedules. The digital twin links the traditional CAD/CAE design process with the actual cradle to grave experience of the component, allowing engineers to re-evaluate assumptions made at the design concept stage, as well as providing the opportunity for lessons learned, informing design for future generations of power plant.