Digital Twin Driven Requirement Conversion in Smart Customized Design

Abstract

Requirement conversion (RC) is an important activity in product customized design. Nowadays, the RC process is mostly driven by designers' knowledge, the RC model is passive to dynamic customer requirements, and the RC behavior is a black box to customers. Digital twin (DT) is characterized as a self-reinforcing mechanism driven by mirroring between the physical and virtual spaces, which is powerful in addressing these challenges for RC. This paper proposes a digital twin driven requirement conversion (DTRC) architecture and a tri-model-based approach integrated by digital model, behavior model, and evolution model for DTRC development. Firstly, the digital model based on the artificial neural network can simulate the virtual twin data to compensate for the absence of real-world data. Then, driven by the virtual-reality integration data, the behavior model mirrors and visualizes the RC behavior in real world based on the decision tree. Finally, a genetic algorithm based evolution model optimizes the RC rules via physical data throughout the whole product life cycle. A case study of DTRC for elevator customized design is further conducted to validate feasibility and effectiveness of the proposed approach. Experimental results show that DTRC outperforms other RC approaches in terms of conversion accuracy. Meanwhile, DTRC can visualize and optimize the conversion path through the tree topology, which is beneficial to the customer participation and proactive to the dynamic environment.