No abstract

No 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.INDEX TERMS Requirement conversion, digital twin, smart customization, tri-model-based approach, elevator customized design.

The conversion from functional requirements (FRs) to design parameters is the foundation of product customization. However, original customer needs usually result in incomplete FRs, limited by customers’ incomprehension on the design requirements of these products. As the incomplete FRs may undermine the design activities afterwards, managers need to develop an effective approach to predict the missing values of the FR. This study proposes an integrative approach to obtain the complete FR. The k nearest neighbor (KNN) algorithm is employed to predict the missing continuous variables in FR, using the improved distance formula for two incomplete FRs. Support vector machine (SVM) classifiers are adopted to classify the missing categorical variables in FR, combined with directed acyclic graph for multi-class classification. KNN and SVM are then integrated into a multi-layer framework to predict the missing values of FR, where categorical and continuous variables both exist. A case study on the elevator customization is conducted to verify that KNN-SVM is feasible in accurate prediction of elevator FR values. Furthermore, KNN-SVM outperforms other five single and five composite methods, with average reduction in root mean squared error (RMSE) of 39% and 21% against KNN and KNN-Tree, respectively.

No abstract