Since the introduction of Industry 4.0, digital twin technology has advanced dramatically, and its applications have been extensively discussed. Through digital twins, real-time connected production systems are anticipated to be more efficient, resilient, and sustainable, facilitating communication and connectivity between digital and physical systems. However, challenges remain in environmental performance and in integration of virtual reality (VR) and artificial intelligence (AI). Further exploration of digital twin technologies is needed to validate the real-world impact and benefits. This paper investigates these challenges by implementing a real-time digital twin based on the ISO 23247, Automation Systems and Integration-Digital Twin Framework for Manufacturing, (Digital twin framework for manufacturing), connecting the physical factory and simulation software with VR capabilities. This digital twin system provides cognitive assistance and a user-friendly interface for operators, thereby improving cognitive ergonomics. The connection of the Internet of Things platform allows the digital twin for real-time bidirectional communication, collaboration, monitoring, and assistance. A laboratory-scale drone factory was used as the digital twin application to test and evaluate the ISO 23247 standard as well as its potential benefits. Additionally, AI integration and environmental performance key performance indicators have been considered as the next stages to improve VR-integrated digital twins. This paper further discusses how digital twins contribute to the improvement of the environmental performance of production systems, primarily from the perspective of data inventory management. Supported by a solid theoretical foundation and a laboratory-scale demonstration of the VR-integrated digital twins in a drone framework, the paper bridges the gap between possible technological integrations and the advancement of digital twins framework based on ISO 23247.
Abstract Model‐Based Product Line Engineering (MBPLE) is an approach that aims at supporting the design and production of families of products. Although various research works and industrial applications show the benefits of MBPLE, there are not yet studies investigating how this approach can be leveraged to support the co‐development of aircraft families together with their manufacturing processes and relative supply chains. Previous work has demonstrated that addressing the simultaneous development of single products and relative production systems (i.e. manufacturing and supply chains) can bring significant advantages in terms of identifying and addressing potential production bottlenecks, reducing costs and increasing the overall value of the aeronautical systems. This paper aims at investigating how the MBPLE approach can be leveraged to support the co‐development of aircraft families, manufacturing processes and supply chains. A tailored 5‐step MBPLE approach is leveraged in this paper addressing multiple variants of the wing designed and produced for the different member products of the Airbus A320 family. Although the proposed approach can address the variability of any system or component, the scope in this paper is limited to the wing to reduce the complexity and size of the application. Other than the wing geometry, dimension and material, the variability affects the manufacturing processes and the organizations of the supply chains, too. In this way, 36 different member products are identified, each one characterized by their assets (e.g. requirements, architectural models). The MBPLE approach is proven to support the management of all the assets of the various member products. Additionally, the fifth step of the tailored MBPLE approach introduces trade‐off analyses: a value‐driven methodology is used in the paper to assess all the product members in terms of aircraft performance (i.e. impact of the member on the aircraft fuel consumption) and production performance (i.e. production cost, time and quality of each member). In this way, it is shown how the MBPLE approach can be used to explore all the different variants, supporting the decision‐making along the whole product line.
