Knowledge-Based Engineering supported by the Digital-Twin: the case of the Power Transformer at EFACEC - Miguel Alves de Azevedo (2020)
Industry 4.0 has made it possible for emerging technologies to revolutionize how organizations operate. New applications, supported by the Internet of things, cyber physical systems, and cloud computing, take advantage of large data exchange networks that capture data from the real and virtual world, to generate valuable insights for product development. This, together with the growing digitalization of product lifecycle information, has made information the most valuable asset of an organization, as it can be applied to improve product design, reduce lead time and decrease monetary costs. However, the growing volume, formats, and purposes of the information an organization captures, also brings challenges for information management, and consequently, appropriate IM and KM instruments and strategies must be adopted to successfully take advantage of organizational knowledge. The adoption of Knowledge-based Engineering can accomplish these goals. KBE refers to the knowledge management tasks of capturing, storing, modeling, coding, and sharing of organizational knowledge, both in explicit form, such as documents, and tacit form, present in the minds of employees. Ultimately, this results in systems that can automate design tasks. Also in the context of technological advances, a new concept called Digital Twin has emerged, which employs bidirectional data transmission to mirror the lifecycle of a physical product, in the virtual realm. Proposed DT functionalities actively use organizational knowledge to improve and automate product design, and as such, this technology can be an adequate vessel for KBE. This dissertation focuses on the implementation of the Digital Twin in power transformer development processes. Using the case of Efacec, a portuguese firm of the energy sector, the DT concept was developed, and this involved defining functionalities that are driven by organizational knowledge to automate, optimize, and streamline PT design tasks, thus accomplishing the goal of KBE. Some of the proposed DT features are the generation of design templates, the identification of design non-conformities, and the capture of engineer feedback. Furthermore, the DT information architecture that is required for these functionalities to successfully be implemented, was envisioned, by defining all captured and generated information in each PT lifecycle phase. Finally, a faceted classification scheme that classifies DT information and enables queries within the DT platform, was developed.