A digital twin framework for product to-be-designed analysis based on operation data

Product design needs to take full account of requirements from the lifecycle of product. However, researcher considered more requirements from product manufacturing/assemble stage, and less requirements from product operation stage. Identifying subsystems to be improved and risk analysis is two methods applied to product design considering the effect of failure at product operation stage. But these methods are labor-intensive and time-consuming tasks and need a lot of expert knowledge. There are huge amounts of data including inspection reports and maintenance records in product operation stage. These data can offer meaningful feedback on next-generation product design. The past few years, digital twin has gained considerable attention because it is characterized by two-way interactions between the digital and physical worlds. This paper proposes a digital twin (DT) framework for product to-be-designed analysis based on operation data. DT data is collected and store from the operation stage of current product. DT data is processed and analyzed to acquire the operation knowledge by Clustering algorithm, Frequent Pattern-growth algorithm, Multi-attribute decision-making, etc. The failure knowledge is used for the design of next-generation product. It can help designer to identify subsystems to be improved and offer the failure information for risk analysis. The failure knowledge also predicts the easily failure subsystems at operation stage of current product. A case of Tunnel Boring Machine is given to illustrate the implementation process of the framework and realize the part function of the framework.