Framework of Digital Twin-Driven Sustainable Intelligent Manufacturing (Pilot)

Type of course:

Digital learning, Lesson

Language:

Duration:

15 minutes

Proficiency:

Intermediate

Target:

Professionals, Manager, Workers

SUMMARY

In this lesson, students will examine the framework of digital twin-driven sustainable manufacturing, which consists of interconnected layers: physical assets, digital models, data processing, intelligence, and service applications. By leveraging real-time data and AI-driven insights, digital twins can predict machine failures, optimize resource use, and minimize energy consumption, promoting sustainability in manufacturing processes. While digital twins present exciting opportunities for efficiency and environmental benefits, challenges like cost, complexity, and data security remain key considerations.

About The Author

The public establishment INTECHCENTRAS was established by the Engineering and Technology Industries Association of Lithuania (LINPRA). As an independent business organization, LINPRA represents the interests of companies in the metal products, machinery and equipment, electromechanics and electronics, plastics, and rubber industries at both the international and national levels. InTechCentras is a Smart Manufacturing competence center. We serve as the coordinators for the Advanced Manufacturing Digital Innovation Hub and were one of the initiators of the EDIH4LT in Lithuania. Intechcentras is the only independent certified RecyClass.

Learning outcomes

By the end of this lesson, students will be able to analyze the framework of digital twin-driven systems in intelligent manufacturing.
By the end of this lesson, students will be able to explain the role of each layer and how they interact to enable real-time data flow, simulation, and optimization in intelligent manufacturing.
By the end of this lesson, students will be able to evaluate the technological components within each layer, such as IoT sensors in the physical layer and AI algorithms in the intelligence layer, and their contributions to system functionality.