Digital twin driven design evaluation

To achieve robust product design with high accuracy and reliability, a digital twin (DT)–driven product design evaluation (PDE) method is presented in this chapter. First, a DT-driven PDE framework, which integrates various stages of product life cycle between virtual and physical spaces, is proposed. In the PDE framework, three complex networks are established: mapping network, prediction network, and feedback network. In order to embed the complex networks accurately in the framework, the PDE algorithms based on artificial neural network are introduced. Finally, a cased study of roll granulator design evaluation is presented to illustrate the application of the proposed PDE method.