Proposal of Digital Twin-Enabled New Product Design Framework

Abstract

Digital twin (DT) has provided numerous opportunities for product development businesses to leverage data acquired from the physical model. Ideally, many companies are improving their use cases by using the collected physical data in a virtual model. Thanks to emerging communication technologies, 5th generation mobile network (5G), the internet of things (IoT), industrial internet of things (IIoT) sensors, etc., which help to collect data from the physical product and send data to cloud computing, called big data. With the gap in the non-availability of standard architecture, many companies are using big data and building their digital models. This paper proposes a novel DT-enabled new product design framework that has built an interaction between the DT and product design environments. This framework enables the data to be captured from various physical entities, stored in a centralized location, and pushed into the digital entity, which processes the data and converts the raw data into information used for decision-making. In the digital entity, multiple technologies are used to process data into information, such as artificial intelligence (AI) and machine learning (ML) models, mathematical models, and simulation models. The processed data that emerges from these models are called synthetic or DT data; this data is later used for control / monitoring and visualization / interaction purposes. In this paper, the data classified as visualization and interaction is integrated with the new product design framework in all four design stages, viz., concept design, detailed design, design verification, and redesign, with various data contributions to the design. A case study was discussed by applying the DT data processed using the AI / ML model in the concept design phase. Using synthetic data, the medium-duty truck frame assembly�s design concept was validated using ten load cases iteratively, and the design was finalized. The results are encouraging that the overall weight of the frame assembly was optimized by 9% without impacting the strength and factor of safety of the frame assembly.