Data-Driven Digital Twin (DT) Framework for Additive Manufacturing

Project: Monitored by Research Administration

Project Details

Grant Program

Faculty Development Competitive Research Grants Program 2022-2024

Project Description

Additive manufacturing (AM) plays a vital role of the fourth industrial revolution. It is widely employed to generate customised products and components in many sectors such as health care, aerospace, automotive and energy. Products and components qualification and certification are essential requirement for these sectors. Currently, additive manufacturing is based on a trial-and-error approach with numerous experimental efforts are conducted. Additionally, large number of specimens are destructively tested to check the product attributes such geometry, microstructures, and mechanical properties. These experiments are required to optimise the manufacturing process parameters within a specific machine which lead to rapidly increase manufacturing cost and lead time. Zero defects and zero physical prototypes are currently the key manufacturing strategy for many industrial companies. Therefore, digital manufacturing is becoming the important enabler towards this strategy.
Digital Twin (DT) is the digital imitation of the real-world product, manufacturing process, service, or system (Singh et. al. 2020). Digital Twin is the ideal solution for data-driven optimisations of additive manufacturing challenges. DT will be 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 as well as 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.
The principal aim of this research is to develop a novel digital twin framework and its related architecture and data models for various types of additive manufacturing process parameters and products attributes. The project will improve our understanding and knowledge in processes, methods and technologies employed in digital twin development. It will also help in introducing digital twin concepts to Kazakhstan industries.
StatusActive
Effective start/end date1/1/22 → 12/31/24

Keywords

  • Digital Twin
  • Additive Manufacturing
  • Digital Manufacturing

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