Conceptualization of Digital Twin-Enabled New Product Design Framework for the Automotive Industry

Abstract The digital twin (DT) has created various opportunities for product development businesses to exploit data obtained from the physical model. Ideally, many companies are strengthening their use cases by incorporating obtained physical data into a virtual model. Emerging communication technologies, such as the 5th generation mobile network (5G), the Internet of Things (IoT), Industrial Internet of Things (IIoT) sensors, and so on, enable the collection of data from physical products and its transmission to cloud computing, also known as big data. With the lack of standard architecture, many businesses use their own big data to develop their digital models without any roadmap. This research provides a novel DT-enabled new product design framework allowing interaction between the DT and product design environments. This system will enable data to be taken from various physical entities, stored in a centralized location, and then fed into a digital entity, which processes the data and converts it into information for decision-making. The digital entity employs various technologies to transform data into information, including artificial intelligence (AI) and machine learning (ML) models, mathematical models, and simulation models. These models generate processed synthetic or DT data, which is then used for control/monitoring and visualization/interaction reasons. In this research, data classed as visualization and interaction is integrated with the new product design framework at all four design stages, namely concept design, detailed design, design verification, and redesign, with varied data contributions to the design. A case study that used DT data processed using the AI / ML model throughout the conceptual design phase was presented. Using synthetic data, the design concept of the medium-duty truck frame assembly was iteratively tested using ten load instances before being finished. The results are encouraging since the overall weight of the frame assembly was reduced by 9% while maintaining the frame assembly's strength and safety factor. The design iterations have come down to 3 iterations from 6 iterations, which is a 50% improvement over the traditional product development processes.