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Digital twin and additive layer manufacturing plays a vital role of the fourth industrial revolution. Digital twin is the ideal solution for data-driven optimisation of additive manufacturing challenges. It is helpful in understating, analysing, and improving 3D printing machining process variables and consequently reducing the number of trial-and-error and component’s non-conformance and shorten product development lead time. Furthermore, the development of genuine digital twin still requires more research efforts to develop a thorough understanding of its concept, data management framework, and development techniques. Therefore, this paper aims to capture important data-driven digital twin requirements for additive layer manufacturing through a systematic approach by identifying the requirements, analysing technologies and processes for digital twin development. The main novelty of this research is applying a holistic approach to build digital twin of additive manufacturing process by capturing the requirements from both literature review and world-class aerospace industrial experts. Overall, the captured requirements will not only serve industries as a basis for implementing digital twin for additive manufacturing and modernize existing data management systems but also opens new research areas in the digital twin domain.

This paper aims to present a detailed Digital Twin (DT) framework indicating important implementation steps and providing insights into DT technology that improves operational efficiency, optimizes performance, and creates a user-friendly platform for real-time monitoring. This project is conducted in collaboration with TechnoGroupService (TGS) company, which specializes in constructing, operating, and maintaining solar and wind power plants in Kazakhstan. The development of DT framework was performed in four phases: project formulation and planning; conducting literature review and analysing the operational processes data transfer procedures of the 50 MW solar power plant; identifying the required specifications of the framework; the development of DT framework and implementation roadmap along with the development of a fully functional prototype. A detailed investigation of the 50MW solar power plant’s operational processes, equipment and sensors, communication networks and data models was performed. The developed framework provides a DT architecture with several layers, including physical infrastructure, data model, modelling and application of DT. Finally, the developed prototype is a handful tool for real-time monitoring and reactive maintenance of the power. The further applications of DT include predictive maintenance and power output forecasting. The obtained results have been reviewed by industry experts to ensure applicability and reliability.