Digital twin designs with generative AI: crafting a comprehensive framework for manufacturing systems

The increasing prevalence of digital twins across various industries is attributed to their capacity for enhancing performance, minimizing downtime, and improving efficiency in the manufacturing process. These virtual models enable users to monitor and analyze the real-time behavior of physical assets, systems, or processes. However, the design of digital twins presents several challenges, such as excessive time costs, security risks, data accuracy, privacy concerns, and complexity in chosen components. This research paper introduces a comprehensive framework that effectively and flexibly addresses these challenges in digital twin development by employing S4 features, a Morphological Matrix, and Fuzzy TOPSIS. The proposed framework draws inspiration from methodologies utilized in product design and leverages a morphological matrix to identify and categorize diverse S4 features. These features are crucial in creating smart, sustainable, sensing, and socially impactful digital twins. Additionally, Fuzzy TOPSIS is employed to determine the optimal digital twin structure. The digital twin is divided into a physical and virtual model, interconnected and interactively communicated through a dedicated interface model. Furthermore, the framework integrates human knowledge into the digital twin structure, enhancing its robustness. When combined with a Large Natural Language Processing model, a comparative analysis of the digital twin structure is presented. A reconfigurable micromachine is used as a case study to validate the proposed approach. The results demonstrate the proposed framework's applicability for developing digital twins within the manufacturing industry. The main contribution of this paper is the innovative integration of human expertise and generative AI in a digital twin design framework. This approach provides a structured method that aligns the solution closely with industry requirements and offers a scalable and adaptable design methodology validated through practical applications. This framework is particularly valuable when dealing with a high number of devices that are challenging for human experts to evaluate rapidly in digital twin design.